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I would like you to choose two articles from the attached files and summarize each article in one paragraph (8 or more sentences, each longer than 10 words). Identify the technological innovation and the advantages noted in the article. Are there any disadvantages you can identify or think of? Be sure to include the title of each article you are summarizing. In a third paragraph, select the technology you think will be the most successful and indicate why.


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Silicon Photonics’ LastMeter Problem Economics and physics still pose challenges to “fiber to the processor” tech By ANTHONY F.J. LEVI NORTH AMERICAN | SEP 2018 | 39 If you think we’re on the cusp of a technological revolution today, imagine what it felt like in the mid-1980s. Silicon chips used transistors with micrometer-size features. Fiber-optic systems were zipping trillions of bits per second around the world. With the combined might of silicon digital logic, optoelectronics, and optical-­f iber communication, anything seemed possible. Engineers envisioned all of these advances continuing and converging to the point where photonics would merge with electronics and eventually replace it. Photonics would move bits not just across countries but inside data centers, even inside computers themselves. Fiber optics would move data from chip to chip, they thought. And even those chips would be photonic: Many expected that someday blazingly fast logic chips would operate using photons rather than electrons. It never got that far, of course. Companies and governments plowed hundreds of millions of dollars into developing new photonic components and systems that link together racks of computer servers inside data centers using optical fibers. And indeed, today, those photonic devices link racks in many modern data centers. But that is where the photons stop. Within a rack, individual server boards are still connected to each other with inexpensive copper wires and high-speed electronics. And, of course, on the boards themselves, it’s metal conductors all the way to the processor. Attempts to push the technology into the servers themselves, to directly feed the processors with fiber optics, have foundered on the rocks of economics. Admittedly, there is an Ethernet optical transceiver market of close to US $4 billion per year that’s set to grow to nearly $4.5 billion and 50 million components by 2020, according to market research firm L ­ ightCounting. But photonics has never cracked those last few meters between the data-center computer rack and the processor chip. Nevertheless, the stupendous potential of the technology has kept the dream alive. The technical challenges are still formidable. But new ideas about how data centers could be designed have, at last, offered a plausible path to a photonic revolution that could help tame the tides of big data. A n y time you access the Web, stream television, or do nearly anything in today’s digital world, you are using data that has flowed through photonic transceiver modules. The job of these transceivers is to convert signals back and forth between electrical and optical. These devices live at each end of the optical fibers that speed data within the data centers of every major cloud service and social media company. The devices plug into switchgear at the top of each server rack, where they convert optical signals to electrical ones for delivery to the group of servers in that rack. The t­ ransceivers also convert data from those servers to optical signals for transport to other racks or up through a network of switches and out to the Internet. Each photonics transceiver module has three main kinds of components: a transmitter containing one or more optical modulators, a receiver containing one or more photodiodes, and CMOS logic chips to encode and decode data. Because ordinary silicon is actually lousy at emitting light, the photons come from SILICON a laser that’s separate from the silicon PHOTONICS CIRCUITRY chips (though it can be housed in the same package with them). Rather than switch the laser on and off to represent bits, the laser is kept on, and electronic bits are encoded onto the laser light by an optical modulator. SILICON SUPPORT OPTICAL MODULATORS OPTICAL PHOTO­ CIRCUITRY (Laser not shown) FIBER DETECTORS This modulator, the heart of the transmitter, can take a few forms. A particuPLUG AND PLAY: A silicon photonics module converts electronic data to photons larly nice and simple one is called the and back again. Silicon circuits [light blue] help optical modulators [bottom row, left] Mach-Zehnder modulator. Here, a narencode electronic data into pulses of several colors of light. The light travels through row silicon waveguide channels the an optical fiber to another module, where photodetectors [gray] turn the light back into electronic bits. These are processed by the silicon circuits and sent on to the laser’s light. The guide then splits in appropriate servers. two, only to rejoin a few millimeters later. Ordinarily, this diverging and converg40 | SEP 2018 | NORTH AMERICAN | SPECTRUM.IEEE.ORG PREVIOUS PAGES: GETTY IMAGES; THIS PAGE: LUXTERA (PHOTO) Inside a Photonics Module But, still, couldn’t chipmakers just integrate the modulator and accept that the chip will have fewer transistors? After all, a chip can now have billions of them. Nope. The massive amount of system function that each square micrometer of a silicon electronic chip area can deliver makes it very expensive to replace even relatively few transistors with lower-­ functioning components such as photonics. Here’s the math. Say there are on average 100 transistors per square micrometer. Then a photonic modulator that occupies a relatively small area of 10µm by 10µm is displacing a circuit comprising 10,000 transistors! And recall that a typical photonic modulator acts as a simple switch, turning light on and off. But each individual transistor can act as a switch, turning current on and off. So, roughly speaking, the opportunity cost for this primitive function is 10,000:1 against the photonic component because there are at least 10,000 electronic switches available to the system designer for every one photonic modulator. No chipmaker is willing to accept such a high price, even in exchange for the measurable improvements in performance and efficiency you might get by integrating the modulators right onto the processor. The idea of substituting photonics for electronics on chips encoun100 TRANSISTORS FIT IN 1 OPTICAL MODULATOR FITS With optica l components on ters other snags, too. For example, 1 SQUARE MICROMETER IN 100 SQUARE MICROMETERS silicon integrated circuits becoming there are critical on-chip functions, increasingly available, you might be such as memory, for which photonPHOTONICS FAIL: Photonics will never be a tempted to think that the integration ics has no comparable capability. real option to transport data from one part of of photonics directly into processor The upshot is that photons are sima silicon chip to another. A single optical switch, a ring oscillator in this case, performs the same chips was inevitable. And indeed, for a ply incompatible with basic comfunction as an individual transistor, but it takes time it seemed so. [See “Linking With puter chip functions. And even when up 10,000 times as much area. Light,” IEEE Spectrum, October 2001.] they are not, integrating a competYou see, what had been entirely ing photonic function on the same underestimated, or even ignored, was the growing mis- chip as electronics makes no sense. match between how quickly the minimum size of features on electronic logic chips was shrinking and how limited pho- Th at’s not to say photonics can’t get a lot closer to protonics was in its ability to keep pace. Transistors today are cessors, memory, and other key chips than it does now. Today, made up of features only a few nanometers in dimension. In the market for optical interconnects in the data center focuses 7-­nanometer CMOS technology, more than 100 transistors on systems called top-of-rack (TOR) switches, into which the for general-­purpose logic can be packed onto every square photonic transceiver modules are plugged. Here at the top micrometer of a chip. And that’s to say nothing of the maze of of 2-meter tall racks that house server chips, memory, and complex copper wiring above the transistors. In addition to other resources, fiber optics link the TORs to each other via the billions of transistors on each chip, there are also a dozen a separate layer of switches. These switches, in turn, connect or so levels of metal interconnect needed to wire up all those to yet another set of switches that form the data center’s gatetransistors into the registers, multipliers, arithmetic logic units, way to the Internet. and more complicated things that make up processor cores The faceplate of a typical TOR, where transceiver modules and other crucial circuits. are plugged in, gives a good idea of just how much data is The trouble is that a typical photonic component, such as a in motion. Each TOR is connected to one transceiver modmodulator, can’t be made much smaller than the wavelength ule, which is in turn connected to two optical fibers (one of the light it’s going to carry, limiting it to about 1 micrometer to transmit and one to receive). Thirty-two modules, each wide. There is no Moore’s Law that can overcome this. It’s not with 40-gigabit-per-second data rates in each direction, can a matter of using more and more advanced lithography. It’s be plugged into a TOR’s 45-millimeter-high faceplate, allowsimply that electrons—having a wavelength on the order of ing for as many as 2.56 terabits per second to flow between few nanometers—are skinny, and photons are fat. the two racks. ing wouldn’t affect the light output, because both branches of the waveguide are the same length. When they join up, the light waves are still in phase with each other. However, voltage applied to one of the branches has the effect of changing that branch’s index of refraction, effectively slowing down or speeding up the light’s wave. Consequently, when light waves from the two branches meet up again, they destructively interfere with each other and the signal is suppressed. So, if you vary a voltage on that branch, what you’re actually doing is using an electrical signal to modulate an optical one. The receiver is much simpler; it’s basically a photodiode and some supporting circuitry. After traveling through an optical fiber, light signals reach the receiver’s germanium or silicon germanium photodiode, which produces a voltage with each pulse of light. Both the transmitter and receiver are backed up by circuitry that does amplification, packet processing, error correction, buffering, and other tasks to comply with the Gigabit Ethernet standard for optical fiber. How much of this is on the same chip as the photonics, or even in the same package, varies according to the vendor, but most of the electronic logic is separate from the photonics. SPECTRUM.IEEE.ORG | NORTH AMERICAN | SEP 2018 | 41 TOR SWITCH SERVERS POWER SHELF MEMORY TOR SWITCH POWER SHELF MEMORY POWER SHELF SERVERS SERVER RACK Data-Center Design TODAY: Photonics slings data through a multitiered network in the data center. The link to the Internet is at the top (core) level. Switchgear moves data via optical fibers to top-of-rack (TOR) switches, which sit atop each rack of servers. But the flow of data within the rack and inside the servers themselves is still done using copper wires. That’s unfortunate, because they are becoming an obstacle to the goal of building faster, more energy-efficient systems. Photonic solutions for this last meter (or two) of interconnect—either to the server or even to the processor itself—represent possibly the best opportunity to develop a truly high-volume optical component market. But before that can happen, there are some serious challenges to overcome in both price and performance. So-called fiber-to-the-processor schemes are not new. And there are many lessons from past attempts about cost, reliability, power efficiency, and bandwidth density. About 15 years ago, for example, I contributed to the design and construction of an experimental transceiver that showed very high bandwidth. The demonstration sought to link a parallel fiberoptic ribbon, 12 fibers wide, to a processor. Each fiber carried digital signals generated separately by four vertical-cavity surface-emitting lasers (VCSELs)—a type of laser diode that shines out of the surface of a chip and can be produced in greater density than so-called edge-emitting lasers. The four VCSELs directly encoded bits by turning light output on and off, and they each operated at different wavelengths in the same fiber, quadrupling that fiber’s capacity using what’s called coarse wavelength-division multiplexing. So, with each VCSEL streaming out data at 25 Gb/s, the total bandwidth of the system would be 1.2 Tb/s. The industry standard today for the spacing between neighboring fibers in a 12-fiber-wide array is 0.25 mm, giving a bandwidth density of about 0.4 Tb/s/mm. In other words, in 100 seconds each 42 | SEP 2018 | NORTH AMERICAN | SPECTRUM.IEEE.ORG TOMORROW: Photonics could facilitate a change in data-center architecture. Rack-scale architecture would make data centers more flexible by physically separating computers from their memory resources and connecting them through an optical network. millimeter could handle as much data as the U.S. Library of Congress’s Web Archive team stores in a month. Data rates even higher than this are needed for fiber-to-theprocesser applications today, but it was a good start. So why wasn’t this technology adopted? Part of the answer is that this system was neither sufficiently reliable nor practical to manufacture. At the time, it was very difficult to make the needed 48 VCSELs for the transmitter and guarantee that there would be no failures over the transmitter lifetime. In fact, an important lesson was that one laser using many modulators can be engineered to be much more reliable than 48 lasers. But today, VCSEL performance has improved to the extent that transceivers based on this technology could provide effective short-reach data-center solutions. And those fiber ribbons can be replaced with multicore fiber, which carries the same amount of data by channeling it into several cores embedded within the main fiber. Another recent, positive development is the availability of more complex digital-­transmission standards such as PAM4, which boosts data transmission rates because it encodes bits on four intensities of light rather than just two. And research efforts, such as MIT’s Shine program, are working toward bandwidth density in fiber-to-theprocessor­to demonstration systems with about 17 times what we achieved 15 years ago. These are all major improvements, but even taken together they are not enough to enable photonics to take the next big leap toward the processor. However, I still think this leap can occur, because of a drive, just now gathering momentum, to change data-center system architecture. ANTHONY F.J. LEVI Today processors, memory, and storage make up what’s called a server blade, which is housed in a chassis in a rack in the data center. But it need not be so. Instead of placing memory with the server chips, memory could sit separately in the rack or even in a separate rack. This rack-scale architecture (RSA) is thought to use computing resources more efficiently, especially for social media companies such as Facebook where the amount of computing and memory required for specific applications grows over time. It also simplifies the task of replacing and managing hardware. Why would such a configuration help enable greater penetration by photonics? Because exactly that kind of reconfigurability and dynamic allocation of resources could be made possible by a new generation of efficient, inexpensive, multi‑terabit-per-second optical switch technology. gration could help. Wafer-scale integration means making photonics on one wafer of silicon, electronics on another, and then attaching the wafers. The paired wafers are then diced up into chips designed to be nearly complete modules. (The laser, which is made from a semiconductor other than silicon, remains separate.) This approach cuts manu­ facturing costs because it allows for assembly and production in parallel. Another factor in reducing cost is, of course, volume. Suppose the total optical Gigabit Ethernet market is 50 million transceivers per year and each photonic transceiver chip occupies an area of 25 square millimeters. Assuming a foundry uses 200-mm-diameter wafers to make them and that it achieves a 100 percent yield, then the number of wafers needed is 42,000. That might sound like a lot, but that figure actually represents less than two weeks of production in a typical foundry. In reality, any given transceiver manufacturer might capture 25 percent of the market and still support only a few days of production. There needs to be a path to higher volume if costs are really going to fall. The only way to make that happen is to figure out how to use photonics below the TOR switch, all the way to the processors inside the servers. The main obstacle to the emergence of this data-­center remake is the price of components and the cost of their manufacture. Silicon photonics already has one cost advantage, which is that it can leverage existing chip manufacturing, taking advantage of silicon’s huge infrastructure and reliability. Nevertheless, silicon and light are not a perfect fit: Apart from their crippling inefficiency at I f s i l i c o n p h o t o n i c s is ever emitting light, silicon components suffer from large optical losses as going to make it big in what are other­ wise all-electronic systems, there will well. As measured by light in to light out, a typical silicon photonic transhave to be compelling technical and ceiver experiences greater than a business reasons for it. The compo10-decibel (90 percent) optical loss. nents must solve an important probThis inefficiency does not matter lem and greatly improve the overall PAST PERFECT: We’ve had the technology to much for short-reach optical intersystem. They must be small, energy bring optical fiber directly to the processor for connects between TOR switches efficient, and super-reliable, and they more than a decade. The author helped conceive this 0.4-terabit-per-second-per-millimeter because, at least for now, the silimust move data extraordinarily fast. demonstrator more than 15 years ago. con’s potential cost advantage outToday, there is no solution that weighs that problem. meets all these requirements, and ­ umble, so electronics will continue to evolve without becoming intiAn important cost in a silicon photonics module is the h yet critically important, optical connection. This is both the mately integrated with photonics. Without significant breakphysical link between the optical fiber and the transmitter or throughs, fat photons will continue to be excluded from places receiver chip as well as the link between fibers. Many hundreds where skinny electrons dominate system function. However, of millions of such fiber-to-fiber connectors must be manufac- if photonic components could be reliably manufactured in tured each year with extreme precision. To understand just very high volume and at very low cost, the decades-old vision how much precision, note that the diameter of a human hair is of fiber-to-the-­processor and related architectures could typically a little less than the 125‑µm d ­ iameter of a s­ ingle-mode finally become a reality. We’ve made a lot of progress in the past 15 years. We have a silica glass fiber used for optical inter­connects. The accuracy with which such single-mode fibers must be aligned in a connec- better understanding of photonic technology and where it can tor is around 100 nm—about one one-thousandth the d ­ iameter and can’t work in the data center. A sustainable multibillion-­ of a human hair—or the signal will become too degraded. New dollar-per-year commercial market for photonic components and innovative ways to manufacture connectors between has developed. Photonic interconnects have become a critifibers and from fiber to transceiver are needed to meet grow- cal part of global information infrastructure. However, the ing customer demand for both precision and low component insertion of very large numbers of photonic components price. However, very few manufacturing techniques are close into the heart of otherwise electronic systems remains just beyond the edge of practicality. to being inexpensive enough. Must it always be so? I think not. n One way to reduce cost is, of course, to make the chips in the optical module cheaper. Though there are other ways to make these chips, a technique called wafer-scale inte- ↗ POST YOUR COMMENTS at https://spectrum.ieee.org/siliconphotonics0918 SPECTRUM.IEEE.ORG | NORTH AMERICAN | SEP 2018 | 43 44 | SEP 2018 | NORTH AMERICAN | SPECTRUM.IEEE.ORG LESS FIRE, MORE POWER Without the needlelike growths that can short out cells, lithium-ion batteries will be safer By Weiyang Li & Yi Cui L I T H I U M - I O N B AT T E R I E S H AV E M A D E H E A D L I N E S for the wrong reason: as a fire hazard. Just this past May, three apparent battery fires in Tesla cars were reported in the United States and Switzerland. In the United States alone, a fire in a lithium-ion battery grounds a flight every 10 days on average, according to the Federal Aviation Administration. And the same problem afflicts electronic cigarettes, which have been blowing up in people’s faces sporadically. No other drawback has so hobbled the advance of what is by far the most promising battery technology to emerge in our lifetimes. Lithium-ion batteries store much more energy than previous chemistries could manage, making them crucial to the future success of phones, drones, cars, even airplanes. Solving this problem would not only protect lives and property, it would also make it possible to use larger battery packs with more closely packed cells. We’d finally be able to fully exploit the great 45 ROOT AND BRANCH: Crystalline lithium-metal structures grow out of the anode of a lithium-ion cell in a branching ­pattern, thus their name, d ­ endrites (from the Greek dendron meaning “tree”). If they grow too long, they can short out the cell. BROOKHAVEN NATIONAL LABORATORY/ SCIENCE SOURCE energy-to-weight ratio, or specific energy, that this technology allows. What’s more, we’d be able to make progress with the next generation of batteries, the ones that use lithium metal. The problems of today’s lithium-ion batteries can be traced largely to dendrites, tiny threadlike structures that form on the surface of an electrode over repeated cycles of charging and discharging. But through our work at Dartmouth and ­Stanford, the two of us have found that a little chemical tweaking of the electrolyte can head off the pesky growths. L I T H I U M - I O N B A T T E R Y P A C K is invariably composed of one or more compartments, or cells, each of which has two electrodes covered by an extremely thin polymer film, called a separator, which prevents their coming into direct contact. Permeating the porous separator is the electrolyte, a material—today generally a liquid—that allows lithium ions to move back and forth during charging and discharging. The slightest damage to the ultrathin separator can put the electrodes into direct contact and create an internal short circuit, which can generate enough heat to make the cell catch fire. The heat of the fire may then overheat adjacent cells, resulting in a chain reaction that can easily cause the whole battery pack to explode. So it’s the integrity of the cell’s separator that matters most. Of course, every effort must be made during the manufacturing process to prevent damage to the separator, but even a perfectly fabricated separator can fail in operation if dendrites later damage it. Dendrites are sharp bits of lithium metal that grow from the anode. These fibers can spread like kudzu vines into the electrolyte, pierce the separator and make their way to the cathode. It’s amazing how 46 | SEP 2018 | NORTH AMERICAN | SPECTRUM.IEEE.ORG ILLUSTRATIONS BY James Provost SHARP DENDRITES FROM TINY IONS GROW Anode SEI Separator Electrolyte Cathode CHARGING: Lithium ions move from the cathode [right] through the separator [middle] and on to the anode [left]. such tiny little things can cause so much destruction: They were responsible, for example, for the fires that grounded the worldwide fleet of Boeing 787s in 2013. Dendrites tend to grow when the battery is overcharged, because that’s when the lithium ions migrating into the anode can no longer find a berth. Normally, the ions slip between the atomic layers of the anode, a process called intercalation, but when the space between the layers is all filled up—as can happen during overcharging—there’s nowhere else for the lithium to go but onto the surface. There they form the seeds of a metallic crystal, which grows with each new charge-discharge cycle. Solving this problem of dendrite growth matters not just for today’s generation of lithium-ion batteries but also for future batteries that will need lithium-metal anodes. That’s because lithium metal has a high theoretical specific energy capacity—3,860 m ­ illiampere-hours per gram—and a negative electrochemical potential no other anode material can match. A higher potential allows for a higher battery voltage, which is just what’s needed in electric cars and in mobile devices. Both these qualities make lithium anodes critical to battery technologies that are still in the lab, like the highly promising lithium-sulfur and lithium-air batteries, which can store 5 to 10 times as much energy by weight as today’s lithium-ion designs. Those future batteries may not be able to i­ ncorporate— as ­lithium-ion batteries do—anodes made of graphite, which has a theoretical capacity of only 372 mAh/g. T H E F O R M AT I O N O F L I T H I U M D E N D R I T E S takes place at the meeting point between the anode and the electrolyte, in a layer called the solid electrolyte interphase (SEI). After OVERCHARGING: When no more room remains for ions, any excess ions will begin to accumulate on the surface. enough lithium ions move into the anode and accept electrons there, the anode finally expands enough to break the SEI layer. From that point onward, lithium begins to form deposits at the broken part of the SEI. And these deposits seed dendrites. Later, during discharging, lithium ions are pulled out of the anode, shrinking it again. The SEI layer collapses, generating more cracks and pinholes from which still more dendrites can begin to shoot out the next time the cell charges. Also, by exposing so much metallic lithium to the electrolyte, these cracks enable the two components to react chemically. As lithium disappears into the resulting chemical product, the lithium that remains for use in the cell diminishes. That decline lowers what’s called the coulombic efficiency, which can be determined by dividing the amount of lithium removed from use by the amount of lithium still participating in the reactions during each charge-discharge cycle. Also, because they are very fragile, dendrites often break off from the anode, generating “dead lithium” that cannot be reused, which further lowers the coulombic efficiency of the cell. To compensate for such losses, today’s batteries must include excess lithium, which adds substantially to their weight and cost. To head off dendrites, we need to shore up the SEI by forming a “super” SEI layer that’s uniform and stable. One way to achieve this is to modify the anode’s surface by laying down an artificial SEI layer, as it were. We’ve tried it, and it works. Unfortunately, this approach greatly complicates the fabrication of lithium-ion cells. Another tactic is to reformulate the electrolyte by including substances that reinforce the SEI layer. The challenge 47 CRYSTALLIZATION: Accumulating ions form metallic crystals, which damage the solid electrolyte interphase (SEI), where the anode meets the electrolyte. BREAKING AND ENTERING: Dendrites branching out from the crystals pierce first the SEI and then the separator, forming a bridge to the cathode—and thus a short circuit. HEADING OFF DENDRITES You can avoid dendrites by shoring up the solid electrolyte interphase layer [yellow] with a “super” layer [light yellow]. A simpler way, now under development, is to add chemicals to the electrolyte. is that such additives must easily dissolve, and most candidate materials don’t—a long-standing problem for battery researchers. The additives that do dissolve quickly are consumed during cycling, and as a result the SEI layers fall apart over the long term. How, then, do you find the right additive? We got our key idea following a roundabout path. W E ’ D B E E N C O N S I D E R I N G T H E P R O B L E M of the lithium­-sulfur battery, the futuristic technology we mentioned earlier. What makes this combination so attractive, in theory, is its ability to store—in the same amount of mass—more than five times the energy of today’s lithium-ion batteries. Such a battery uses metallic lithium for the anode and sulfur for the cathode, and during the reactions that take place while charging or discharging there are a number of steps that create intermediate ­products at the sulfur cathode. These products, known as ­polysulfide ions, are highly soluble in the electrolyte, and that means that when the battery is in operation they can travel from the cathode, pass through the separator, and then arrive at the anode. That’s not good: Only the lithium ions ought to get that far. When these polysulfide ions hit the anode, they react vigorously with the lithium, accept electrons, and are reduced to a solid. Not only does this process slowly deplete sulfur from the system, it also gradually forms a coating that can wreck the lithium anode. This has been the main difficulty dogging the development of ­lithium-sulfur batteries. To avoid this parasitic reaction, researchers have mainly tried to restrict the polysulfide from leaching out of the sulfur cathode in the first place. In one of our brainstorming sessions, we began to think different, as Steve Jobs might say: What if we could actually take advantage of this reaction? By controlling how the polysulfide ions react with lithium, perhaps we could not just form a strong and stable SEI layer but actually nip dendrites in the bud! Meanwhile, we discovered that lithium nitrate—a very commonly used lithium salt—had long been considered as a potential electrolyte additive because of its ability to restrict— or ­passivate—the reactivity of the lithium metal. Perhaps by adding both polysulfide and lithium nitrate to the electrolyte 48 | SEP 2018 | NORTH AMERICAN | SPECTRUM.IEEE.ORG we could create complementary actions: Polysulfide reacts with lithium metal, while lithium nitrate can help to prevent the lithium from reacting with polysulfide. By manipulating these two competing reactions, we should be able to turn the sulfur-lithium reaction from a flaw to a feature. We added lithium polysulfide and lithium nitrate to the electrolyte in various concentrations. We studied the effect on the process of lithium plating and stripping in a two-­electrode test cell that used lithium metal as one electrode and a s­ tainless-steel foil as the other. We assembled coin cells, also called button cells, similar to the ones that power small electronic devices, such as watches, calculators, and hearing aids, and we applied a constant current during charging, allowing the same current to flow during discharge. We deposited a bit of lithium onto the stainless steel by charging the cell; then we stripped it off in discharge, repeating the cycle many times. Finally, we took the cell apart and examined the lithium deposit under a scanning electron microscope. What we saw was intriguing. Without electrolyte additives, the plated lithium formed structures that were thin, sharp, and fiberlike—dendritic, in a word. But when we added lithium nitrate to the electrolyte, the deposited lithium was thicker, less sharp, and shaped more or less like a n ­ oodle. The lithium nitrate had moderated dendrite growth but not prevented it. Next, we added both lithium polysulfide and lithium nitrate to the electro­lyte in various quantities. At just the right balance of additives, the synergistic effect we’d sought came through: No harmful dendritic structures grew. Instead we got flat, ­pancake-shaped lithium deposits. Even after hundreds of charge-discharge cycles, the surface of the plated lithium was still flat, without any dendritic structures. Besides heading off dendrites, our two additives together greatly enhanced the coulombic efficiency and the cycling stability. The coulombic efficiency was better than 99 percent over more than 300 charge-discharge cycles. Charging caused plating on only a tiny bit more lithium than was stripped off during discharge. In contrast, with lithium nitrate alone, coulombic efficiency drops to less than 92 percent after just 180 cycles, and with polysulfide alone it’s only about 80 percent. These two additives, working to­­gether, bring a huge improvement because of their effect on the SEI layer. To figure out the exact mechanism of that effect, we used a technique called X-ray photoelectron spectroscopy and also conventional electron microscopy to deduce the structure and chemical composition of the SEI layer. In cells using one or the other additive, we found that the SEI layers were marred by lots of cracks and pinholes. When both additives were present, though, we got a flat, uniform SEI. And the chemical breakdown of the SEI layers confirmed that the two additives indeed had competing effects. When we added both lithium nitrate and polysulfide, the lithium nitrate was the first to react with the lithium metal, and it did passivate the metal’s surface, as expected, drastically reducing the metal’s reaction with the polysulfide. The product from the first reaction formed mainly in the upper layer of the SEI, and it effectively suppressed the formation of dendrites. This technique for preventing the growth of dendrites is still in its early days. We have problems to solve before we can think about commercialization. A particular difficulty is finding the precise formulation of the electrolyte additives for each of the several different kinds of lithium batteries. But this new strategy holds out the promise not only of creating a safer, higher-energy lithium-ion battery but also of paving the way for next­-generation battery chemistries. With dendrites defeated, a lithium-metal design could store far more energy than today’s batteries while lasting through the many charging cycles that consumer products require. We predict that in another 5 to 10 years, our technology will allow the commercialization of safe, superhigh-capacity batteries for phones, laptops, cars, and airplanes. That would make headlines for rechargeable batteries of a much more positive sort. n ↗ POST YOUR COMMENTS online at https://spectrum.ieee.org/dendrites0918 STANFORD UNIVERSITY HIGH THREAD COUNT: Dendrites take on a threadlike form as they grow on the surface of the electrode. RED LIGHT, GREEN LIGHT— NO LIGHT Tomorrow’s communicative cars could take turns at intersections By Ozan K. Tonguz • • • Photography by Dan Saelinger 24 | OCT 2018 | NORTH AMERICAN | SPECTRUM.IEEE.ORG SPECTRUM.IEEE.ORG | NORTH AMERICAN | OCT 2018 | 25 ○ Life is short, and it seems shorter still when you’re in a traffic jam. Or sitting at a red light when there’s no cross traffic at all. ○ In Mexico City, São Paolo, Rome, Moscow, Beijing, Cairo, and ­Nairobi, the morning commute can, for many exurbanites, exceed 2 hours. Include the evening commute and it is not unusual to spend 3 or 4 hours on the road every day. ○ Now suppose we could develop a system that would reduce a two-way daily commute time by a third, say, from 3 to 2 hours a day. That’s enough to save 22 hours a month, which over a 35-year career comes to more than 3 years. Take heart, beleaguered commuters, because such a system has already been designed, based on several emerging technologies. One of them is the wireless linking of vehicles. It’s often called vehicle-to-vehicle (V2V) technology, although this linking can also include road signals and other infra­structure. Another emerging technology is that of the autonomous vehicle, which by its nature should minimize commuting time (while making that time more productive into the bargain). Then there’s the Internet of Things, which promises to connect not merely the world’s 7 billion people but also another 30 billion sensors and gadgets. All of these technologies can be made to work together with an algorithm my colleagues and I have developed at Carnegie Mellon University, in Pittsburgh. The algorithm allows cars to collaborate, using their onboard communications capabilities, to keep traffic flowing smoothly and safely without the use of any traffic lights whatsoever. We’ve spun the project out as a company, called Virtual Traffic Lights (VTL), and we’ve tested it extensively in simulations and, since May 2017, in a private project on roads near the Carnegie Mellon campus. In July, we demonstrated VTL technology in public for the first time, in Saudi Arabia, before an audience of about 100 scientists, government officials, and representatives of private companies. The results of that trial confirmed what we had already strongly suspected: It is time to ditch the traffic light. We have nothing to lose except countless hours sitting in our cars while going nowhere. ••• The principle behind the traffic light has hardly changed since the device was invented in 1912 and deployed in Salt Lake City, and two years later, in Cleveland. It works on a timer-based approach, which is why you sometimes find 26 | OCT 2018 | NORTH AMERICAN | SPECTRUM.IEEE.ORG VTL Algorithm: Letting Cars Control Their Own Traffic Transceivers (using IEEE Standard 802.11p) send out a basic safety message every tenth of a second. The message tells recipients where the transmitting vehicle is by latitude, longitude, and heading. Latitude: 40.719969 Longitude: -73.844283 Cars “Elect” a Leader— 1. Each vehicle computes its own distance to the intersection, the distance of the vehicles approaching the inter­section from other directions, and each vehicle’s speed, acceleration, and trajectory. Together they elect one vehicle to serve as the leader for a certain amount of time. 2. The leader vehicle decides which direction has the rightof-way (the equivalent of a green light) and which direction has the red light. LEADER 3. The leader assigns the status of a red light to its own direction of movement, while giving the green light to all the cars in the perpen­ dicular flow. ILLUSTRATIONS BY Anders Wenngren 40.719994, -73.844846 40.720282, -73.844712 VTL Algorithm The Virtual Traffic Lights (VTL) algorithm takes that vehicle’s data, adds it to data from nearby vehicles, and compares it with readouts from digital mapping services. LEADER Then Follow Its Orders 4. After the leader’s time is up, a car in the perpendicular flow becomes the leader and does the same thing. In this fashion, leadership is handed over repeatedly. That’s all the algorithm needs to decide which vehicle gets to go through the intersection (green light) and which has to stop (red light). yourself sitting behind a red light at an intersection when there are no other cars in sight. The timing can be adjusted to match traffic patterns at different points in the commuting cycle, but that is about all the fine-tuning you can do, and it’s not much. As a result, a lot of people waste a lot of time. Every day. Instead, imagine a number of cars approaching an inter­section and communicating among themselves with V2V technology. Together they vote, as it were, and then elect one vehicle to serve as the leader for a certain period, during which it decides which direction is to be yielded the right-of-way—the equivalent of a green light—and which direction has the red light. So who has the right-of-way? It’s very simple, and deferential. The leader assigns the status of a red light to its own direction of movement while giving the green light to all the cars in the perpendicular flow. After, say, 30 seconds, another car—in the perpendicular flow—becomes the leader and does the same thing. Thus, leadership is handed over repeatedly, in a roundrobin fashion, to fairly share the responsibility and burden— because being the leader does involve sacrificing immediate self-interest for the common good. With this approach, there is no need at all for traffic lights. The work of regulating traffic melts invisibly into the wireless infrastructure. You would never find yourself sitting at a red light when there was no cross traffic to contend with. Our company’s VTL algorithm elects leaders by consulting such parameters as the distance of the front vehicle in each approach from the center of the intersection, the vehicles’ speed, the number of vehicles in each approach, and so on. When all else is equal, the algorithm elects the vehicle that’s farthest from the intersection, so it will have ample time to decelerate. This policy ensures that the vehicle that’s closest to the intersection gets the right-of-way—that is, the virtual green light. It’s important to note that VTL technology needs no camera, radar, or lidar. It gets all the orientation it needs from a wireless system called dedicated short-range communications. DSRC refers to radio schemes, including dedicated bandwidth, that were developed in the United States, Europe, and Japan between 1999 and 2008 to let nearby cars communicate wirelessly. DSRC developers envisioned various uses, including electronic toll collection and cooperative adaptive cruise control—and also precisely the function we are using it for, intersection collision avoidance. Very few production cars are now equipped with DSRC transceivers (and it’s possible that emerging 5G wireless technology will supersede DSRC). But such transceivers are readily available, and they provide all the functionality we need. These transceivers, designed to make use of IEEE Standard 802.11p, must each send out a basic safety message every tenth of a second. The message tells recipients where the transmitting vehicle is by latitude, longitude, and heading. Running on a processor in a vehicle, our VTL algorithm takes the data from that vehicle, throws in whatever it is receiving from neighboring vehicles, and overlays the result onto readouts from such digital mapping services as Google Maps, Apple Maps, or OpenStreetMap. In this way, each vehicle can compute its own distance to the intersection as well as the distance SPECTRUM.IEEE.ORG | NORTH AMERICAN | OCT 2018 | 27 of the vehicles approaching the intersection from the other directions. It can also compute each vehicle’s speed, acceleration, and trajectory. That’s all the algorithm needs to decide who gets to go through the intersection (green light) and who has to stop (red light). And once the decision has been made, a head-up display in each car displays the light to the driver from a normal viewing position. Of course, the VTL algorithm solves only the problem of managing traffic at inter­sections, stop signs, and yield signs. It doesn’t drive the car. But when functioning within its proper domain, VTL can do everything at a much lower cost than autonomous vehicle technology can. Self-driving cars require far more computing capability just to make sense of the individual data feeds coming from their lidar, radar, cameras, and other sensors, and more still to fuse those feeds into a single view of the surroundings. Think of our method as the substitution of a rule of thumb for true intelligence. The VTL algorithm lets the cars control their own traffic much as colonies of insects and schools of fish do. A school of fish shifts direction all at once, without any master conductor directing the members of the school; instead, each fish takes its cue from the movement of its immediate neighbors. This is an example of a completely distributed system behavior as opposed to a centralized network behavior. With it, fleets of vehicles in a city can manage traffic flow by themselves without a centralized control mechanism and without any human intervention—no police, no traffic lights, no stop signs, and no yield signs. W ••• e didn’ t in vent the c oncep t of intelligent inter­sections, which dates back decades. One early idea was to place a magnetic coil under the asphalt surface of a road to detect the approach of vehicles along a single route to an inter­section and then adjust the duration of the green and red phases accordingly. Similarly, cameras placed at intersections can be used to count the vehicles in each approach and compute how best to time the lights at an intersection. But both technologies are expensive to install and maintain and therefore only a few intersections have been fitted out with them. We started by running our VTL algorithm on a virtual model for two cities: Pittsburgh and Porto, Portugal. We took traffic data 28 | OCT 2018 | NORTH AMERICAN | SPECTRUM.IEEE.ORG from the U.S. Census Bureau and the corresponding P ­ ortuguese agency, added map data from Google Maps, and fed it all into SUMO, the Simulation of Urban Mobility, an open-source software package developed by the German Aerospace Center. SUMO simulated the rush-hour commuting time under two scenarios, one using the existing traffic lights, the other using our VTL algorithm. It found that VTL reduced the average commute to 21.3 minutes from 35 minutes in Porto and to 18.3 minutes from 30.7 minutes in Pittsburgh. Reductions for people commuting into the city from the suburbs and beyond were cut by a minimum of 30 percent and a maximum of 60 percent. Importantly, the variance of the commute time— a statistical measure of how much a quantity diverges from the mean value—was also reduced. Those time savings came primarily for two reasons. First, VTL eliminated the time spent waiting at a red light when there were no cars crossing at right angles. Second, VTL introduced traffic control to every intersection, not just those that have active signals. So it was not necessary for cars to stop at a stop sign, for example, when no other cars were around. Our simulations showed other benefits—ones that are arguably more important than saving time. The number of accidents was reduced by 70 percent, and—no surprise—most of the reduction was centered at the intersections, stop signs, and other interchanges. Also, by minimizing the time spent dawdling at intersections and accelerating and decelerating, VTL measurably reduces the average car’s carbon footprint. So, what would it take to get VTL technology out of the lab and into the world? To begin with, we’d have to get DSRC into production cars. In 2014, the U.S. National Highway Traffic Safety Administration proposed the adoption of the technology, but the Trump administration hasn’t yet implemented the regulation, and it’s not clear what the final decision will be. So U.S. manufacturers may now be reluctant to install DSRC transceivers, given that they’d add cost to a car and they’d be useful only if other cars carry them, too—the familiar chicken-and-egg problem. And until enough cars begin to carry the devices, the scale of manufacturing will remain low and the unit cost high. In the United States, only ­General Motors has begun to put DSRC radios into cars, all of them high-end ­Cadillacs. However, in Europe and Japan the outlook is a lot more favorable. A number of European automakers have committed to putting the transceivers in their PHOTOGRAPH BY Dan Saelinger cars, and earlier this year in Japan, where the government strongly supports the technology, auto giant Toyota reiterated its commitment. And even if DSRC fails entirely, our VTL algorithm can be implemented with other wireless technologies, such as 5G or Wi-Fi. The concept of incomplete penetration of DSRC transceivers brings up one of the biggest potential obstacles to adoption of our VTL technology. Could it still work even if only a certain percentage of vehicles is equipped with DSRC? The answer is yes, provided that governments equip existing traffic signals with DSRC technology. Governments may well be willing to do that, if only because they would rather not do away with hundreds of billions of dollars’ worth of existing signal infrastructure. To address this problem, we’ve fitted out our Virtual Traffic Lights technology with a short-term solution: We can upgrade existing traffic lights so that they can detect the presence of DSRC-equipped vehicles in each approach and decide the green-red phases accordingly. The beauty of this scheme is that all vehicles could make use of VTL REDUCED the same roads and intersections, THE AVERAGE whether or not they are equipped with DSRC. This approach may not COMMUTE TO reduce commute time as much as the 21.3 MINUTES ideal VTL solution, but even so it is at FROM 35 least 23 percent better than the current traffic control systems, according MINUTES IN to both our simulations and to field PORTO AND TO trials in Pittsburgh. 18.3 MINUTES Yet another challenge is how to hanFROM 30.7 dle pedestrians and bicyclists. Even in a regime mandating DSRC transceiv- MINUTES ers for all cars and trucks, we couldn’t IN PITTSBURGH reasonably expect cyclists to install the devices or pedestrians to carry them. That might make it hard for those people to cross busy intersections safely. Our solution for the short term, while physical traffic signals still coexist with the VTL system, is to provide pedestrians a way to give themselves the right-of-way. Ever since January of this year, our pilot program in Pittsburgh has provided a button to push that actuates a red light—real for the pedestrians, and virtual for the cars—at all four approaches to the inter­ section. It has worked every time. In the longer term, the bicyclist and pedestrian challenge might be solved with Internet of Things technology. As the IoT expands, the day will finally come when everyone carries a DSRC-capable device at all times. Meanwhile, under ideal conditions, with no physical signals at all, we have demonstrated that the vehicles voting on how to assign right-of-way can allot a portion of the signaling cycle to pedestrians. During these interludes, a virtual red light shines in all the vehicles at all four approaches, lasting long enough for any pedestrians there to cross safely. This preliminary solution wouldn’t be optimal for traffic flow, and so we are also working on a method using cheap ­dashboard-mounted cameras to spot pedestrians and give them the right-of-way. ••• Ultimately, what makes virtual traffic ­s ignals so promising is the advent of self-driving vehicles. As envisioned today, such vehicles would do everything human drivers now do—stopping at traffic lights, yielding at yield signs, and so forth. But why automate transportation halfway? It would be far better to make such vehicles fully autonomous, managing traffic without any conventional signs or signals. The key in achieving this goal is V2V and vehicle-toinfrastructure communications. This matters because today’s self-driving cars are often unable to negotiate their way into and out of busy intersections. This is one of the hardest technical problems, and it continues to challenge even industry leader Waymo (a subsidiary of Google’s parent company, Alphabet). In our simulations and field trials, we have found that autonomous vehicles equipped with VTL can manage intersections without traffic lights or signs. Not needing to identify such objects greatly simplifies the computer-vision algorithms that today’s experimental self-driving cars rely on as well as the computational hardware that runs those algorithms. These elements, together with the sensors (especially lidar), constitute the single costliest part of the package. Because VTL has a largely modular software architecture, it would be easy to integrate it into the rest of an autonomous car’s software. Furthermore, VTL can solve most, if not all, of the hard problems related to computer vision—say, when the sun shines straight into a camera, or when rain, snow, sandstorms, or a curving road obscure the view. To be clear, VTL is not really competing with the technology of self-­driving cars; it is complementing it. And that alone would help to speed up the robocar rollout. Well before then, we hope to have our system up and running for human-driven cars. Just this past July we staged our first public demonstration, in Riyadh, Saudi Arabia, in heat topping 43 °C (100 °F), with devices installed in the test vehicles. Representatives from government, academia, and c­ orporations—­including Uber—boarded a Mercedes-Benz bus and drove through the campus of the King Abdulaziz City for Science and Technology, crossing three intersections, two of which had no traffic lights. The bus, together with a GMC truck, Hyundai SUV, and a Citroën car, engaged the intersections in every possible way, and the VTL system worked every time. When one driver deliberately disobeyed the virtual red light and attempted to cross, our safety feature kicked in right away, setting off a flashing red light for all four approaches, heading off an accident. I hope and believe that this was a turning point in transportation. Traffic lights have had their day. Indeed, they lasted over a century. Now it’s time to move on. n ↗ POST YOUR COMMENTS at https://spectrum.ieee.org/v2v1018 SPECTRUM.IEEE.ORG | NORTH AMERICAN | OCT 2018 | 29 ST • I 0.2 ST • II 0.9 ST • III -0.3 ST • AVR 0.6 ST • AVL 0.0 ST • I 0.3 ST • II 0.6 ST • III 0.2 ST • AVR -0.4 ST • AVL 0.0 10:40am 10:41am 10:42am 10:43am 10:44am 10:45am HR Spo2 120 50 100 90 rr 30 8 93 65 23 12742.6 42.2 127 42.2 36.942.6 42.2 42.636.9 42.6 36.9 36.9 TEMP PULSE TEMP TEMP (126) 160 90 128/76 (79) rr 30 8 97/73 40 113/53(68) 118/69(81) 113/53(68) 113/53(68) 113/53(68) 113/53(68) 93/60(66) 118/69(81) 118/69(81) 118/69(81) 118/69(81)161/125(133) 93/60(66) 93/60(66) 93/60(66) 93/60(66) 113/53(68) 161/125(133)161/125(133)161/125(133)161/125(133) 118/69(81) 113/53(68) 113/53(68) 113/53(68) 113/53(68) 93/60(66) 118/69(81) 118/69(81) 118/69(81) 118/69(81)161/125(133) 93/60(66) 93/60(66) 93/60(66) 93/60(66) 161/125(133)161/125(133)161/125(133)161/125(133) 93/60(66) ) 161/125(133) 113/53(68) 118/69(81) 93/60(66) ) 161/125(133) AI IN TH E SPECTRUM.IEEE.ORG | NORTH AMERICAN | OCT 2018 | 31 IN A HOSPITAL’S INT E NSIVE CARE UNIT (ICU), IN THE INTENSIVE CARE UNIT, ARTIFICIAL INTELLIGENCE CAN KEEP WATCH BY BEHNOOD GHOLAMI, WASSIM M. HADDAD & JAMES M. BAILEY ILLUSTRATIONS BY MCKIBILLO the sickest patients receive round-theclock care as they lie in beds with their bodies connected to a bevy of surrounding machines. This advanced medical equipment is designed to keep an ailing person alive. Intravenous fluids drip into the bloodstream, while mechanical ventilators push air into the lungs. Sensors attached to the body track heart rate, blood pressure, and other vital signs, while bedside monitors graph the data in undulating lines. When the machines record measurements that are outside of normal parameters, beeps and alarms ring out to alert the medical staff to potential problems. • While this scene is laden with high tech, the technology isn’t being used to best advantage. Each machine is monitoring a discrete part of the body, but the machines aren’t working in concert. The rich streams of data aren’t being captured or analyzed. And it’s impossible for the ICU team—critical-care physicians, nurses, respiratory therapists, pharmacists, and other specialists—to keep watch at every patient’s bedside. • The ICU of the future will make far better use of its machines and the continuous streams of data they generate. Monitors won’t work in isolation, but instead will pool their information to present a comprehensive picture of the patient’s health to doctors. And that information will also flow to artificial intelligence (AI) systems, which will autonomously adjust equipment settings to keep the patient in optimal condition. At our company, Autonomous Healthcare, based in H ­ oboken, N.J., we’re designing and building some of the first AI systems for the ICU. These technologies are intended to provide vigilant and nuanced care, as if an expert were at the patient’s bedside every second, carefully calibrating treatment. Such systems could relieve the burden on the overtaxed staff in c­ ritical-care units. What’s more, if the technology helps patients get out of the ICU sooner, it could bring down the skyrocketing costs of health care. We’re focusing initially on hospitals in the United States, but our technology could be useful all around the world as populations age and the prevalence of chronic diseases grows. The benefits could be huge. In the United States, ICUs are among the most expensive components of the health care system. About 55,000 patients are cared for in an ICU every day, with the typical daily cost ranging from US $3,000 to $10,000. The cumulative cost is more than $80 billion per year. As baby boomers reach old age, ICUs are becoming increasingly important. Today, more than half of ICU patients in the United States are over the age of 65—a demographic group that’s expected to grow from 46 million in 2014 to 74 million by 2030. Similar trends in Europe and Asia make this a worldwide problem. To meet the growing demand for acute clinical care, ICUs will need to increase their capacity as well as their capabilities. Training more critical-care specialists is part of the solution—but so is automation. Far from replacing humans, AI systems could become part of the medical team, allowing doctors and nurses to deploy their skills when and where they’re needed most. I N I C U S T O D AY, the data from the raft of bedside monitors is usually lost as the monitor screens refresh every few seconds. While some advanced ICUs are now trying to archive these measurements, they still struggle to mine the data for clinical insights. A human doctor typically has neither the time nor the tools to make sense of the rapidly accumulating data. But an AI system does. It could also take actions based on the data, such as adjusting the machines involved in crucial ICU tasks. At Autonomous Healthcare, we’re focusing first on AI systems that could manage a patient’s ventilation and fluids. Mechanical ventilators come into play when a patient is sedated or suffers lung failure, a common ICU condition. And careful fluid management maintains the proper volume of blood flowing through a patient’s circulatory system, therefore ensuring that all the tissues and organs get enough oxygen. Our methodologies spring from an unlikely source: the aerospace industry. Two of us, Haddad and Gholami, are aerospace control engineers. We met at Georgia Tech’s school of aero32 | OCT 2018 | NORTH AMERICAN | SPECTRUM.IEEE.ORG space engineering, where Haddad is a professor of dynamical systems and control and Gholami formerly worked as a doctoral researcher. Bailey joined the collaboration in the early 2000s when he was an associate professor of anesthesiology at the Emory University school of medicine. H ­ addad and B ­ ailey first worked on control methods to automate anesthesia dosing and delivery in the operating room, which we tested in clinical studies at Emory University Hospital, in Atlanta, and Northeast Georgia Medical Center, in Gainesville, Ga. We then set our sights on more complex and broader control problems for the ICU. In 2013, Haddad and Gholami founded Autonomous Healthcare to commercialize our AI systems. Gholami is the company’s CEO, Haddad is chief science advisor, and Bailey is chief medical officer. How is aerospace similar to medicine? Both fields involve vast amounts of data that must be processed quickly to make decisions while lives hang in the balance, and both require that many tasks be performed simultaneously to keep things running smoothly. In particular, we see a role for feedback control technology in critical-care medicine. These technologies use algorithms and feedback to modify the behavior of engineered systems through sensing, computation, and actuation. They have become ubiquitous in the safety-critical systems of flight control and air traffic control. However, there’s a key difference between an airplane and a hospital patient. An airplane’s design and control is based on well-established theories of mechanics and aerodynamics, whereas the human body involves highly complex biological systems that function and interact in ways we don’t yet entirely understand. Consider the management of mechanical ventilation. ICU patients may require this support because of direct trauma, lung infection, heart failure, or an inflammatory syndrome such as sepsis. The ventilator alternates between forcing air into the lungs and allowing the lungs to passively deflate. The device can be dialed up or down to do all of the work or to assist the patient’s own efforts. The interaction between human and machine is a subtle thing to manage. The human body has its own automatic mechanism to govern breathing, in which the nervous system triggers the diaphragm muscle to contract and pull downward on the lungs, thus initiating the intake of air. The ventilator must work with this innate drive; it should be synchronized with the patient’s natural transitions between inhaling and exhaling, and it should match the natural air volume of the patient’s breathing. Unfortunately, mismatches between the patient’s demand and the machine’s delivery are all too common, which can cause a patient to “fight the ventilator.” For example, a patient may naturally need more time to inhale, but the ventilator transitions to the exhalation prematurely. This and other synchronization problems with mechanical ventilation are associated with longer stints on the ventilator, longer stays in the ICU, and increased risk of death. Experts don’t yet know why asynchrony has these detrimental effects, but patients clearly experience discomfort when trying to breathe out while the machine is 2 3 53% 1 12% MECHANICAL VENTILATOR 4 ADAPTIVE CONTROLLER who need help breathing are put on mechanical ventilators [1]. These machines push air into the lungs, but the rhythm can get out of sync with natural breathing patterns, causing patients to “fight the ventilator.” A smart control system could read airflow measurements [2] and identify different types of ventilator asynchrony [3] in real time via a machine-learning algorithm. In a fully autonomous system, an adaptive controller [4] would constantly adjust the ventilator’s airflow to keep it in sync with the patient. As a step toward the goal of full autonomy, a similar system could be used as a decisionsupport tool in the ICU, providing recommendations that respiratory therapists could use to make adjustments. BREATHING EASIER CRITICALLY ILL PATIENTS pushing air into their lungs, and their laboring muscles experience an additional workload. In ICUs in the United States, the share of patients on ventilators who experience severe asynchrony has been estimated to be between 12 and 43 percent. The first step in addressing this problem is to detect it. Experienced respiratory therapists can identify different types of asynchrony if they continuously monitor the waveforms on a ventilator’s display screen indicating the pressure and flow. But in an ICU, one respiratory therapist typically oversees 10 or more patients and can’t possibly monitor all of them constantly. ILLUSTRATION BY MCKIBILLO At our company, we’ve designed a machine-learning framework that replicates that human expertise in detecting different types of asynchrony. To train our system, we used a data set of waveforms from patients on ventilators, in which each waveform had been evaluated by a panel of clinical experts. Our algorithm learned the signatures of different asynchrony types—such as a particular dip in the flow signal at a specific point in time. In our first assessments of the algorithm’s performance, we focused on what’s called cycling asynchrony, which is the most challenging type to detect. SPECTRUM.IEEE.ORG | NORTH AMERICAN | OCT 2018 | 33 2 1 HEMODYNAMIC MONITOR INFUSION PUMP AND IV 3 PHYSIOLOGICAL MODEL 4 ADAPTIVE CONTROLLER FLUID MOVEMENTS 14 501 99 MOST ICU PATIENTS require infusion pumps and IVs [1] to drip fluid into their veins. Getting the fluid volume right is crucial: If levels are either too low or too high in the circulatory system, serious complications can arise. A smart control system could track real-time measurements [2] such as arterial blood pressure and the amount of blood pumped by the heart; the system could then feed the data into a physiological model [3] that represents how fluids move through the body’s blood vessels and tissues. In a fully autonomous system, an adaptive controller [4] could continuously adjust fluid inputs to keep the patient stable. Initially, ICU physicians could use the technology as a decision-support system that provides recommendations. Here the ventilator’s initiation of the exhale doesn’t match the patient’s own exhalation. The accuracy of our algorithm in detecting cycling asynchrony in a new data set matched that of human experts. We’re now testing the algorithm at Northeast Georgia Medical Center’s ICU to detect respiratory asynchrony in real patients and in real time. The technology has been incorporated into a clinical-decision support system, which is designed to help respiratory therapists assess a patient’s needs. This framework can also provide researchers with a tool to better understand the underlying causes of asynchrony and its 34 | OCT 2018 | NORTH AMERICAN | SPECTRUM.IEEE.ORG impact on patients. Our long-term goal is to design mechanical ventilators that can automatically adjust their own settings in response to each patient’s needs. W H E N Y O U P I C T U R E A N I C U , your mental image probably includes patients with plastic bags hanging from stands by their bedsides, fluids continually dripping into their veins through IVs. About 75 percent of patients require such fluid management at some point during their stay in the ICU. However, calibrating the correct amount of fluid is far from an exact science. Just tracking a patient’s fluid levels is a hard ILLUSTRATION BY MCKIBILLO task: No existing medical sensors can directly monitor fluid volume, so doctors rely on indirect indicators like blood pressure and urine volume. The amount of fluids that patients need depends on their illness and medications, among other things. Getting the fluids right is particularly important for patients with sepsis, a life-threatening syndrome characterized by inflammation throughout the body. In these patients, the blood vessels dilate, thus reducing blood pressure, and fluid leaks from the tiniest vessels, the capillaries. As a result, less oxygen-carrying blood reaches the organs, which can cause organs to fail and patients to die. Doctors combat sepsis by dispensing drugs to boost blood pressure and pumping extra fluids into patients’ circulatory systems. It’s important to add enough f luid, but not too much— an excess can cause complications such as pulmonary edema, a buildup of fluid in the lungs that can interfere with breathing. Studies have shown that fluid overload is associated with more days on mechanical ventilators, longer stays in the hospital, and higher rates of mortality. Doctors therefore aim to maintain their patients’ fluids at certain levels, which are based on models for an average patient. When the doctors come through the ICU on their rounds, they try to determine whether the patient is holding steady at the goal level by checking the mix of gases in the blood and monitoring blood pressure and urine output. Deciding when to add fluids and how much to add is highly subjective, and there’s considerable debate about the best practices. An AI system could do better. Rather than basing its decisions on goals established for an average patient, it could analyze a wide variety of physiological indicators for an individual patient in real time, and continuously dispense fluids according to that patient’s specific needs. At Autonomous Healthcare, we’ve developed a fully automated system that looks at indirect measurements of a patient’s fluid levels (such as blood pressure and variation in the volume of blood pumped out by each heartbeat) and then feeds the data into a sophisticated physiological model. Our system uses these measurements to assess how fluids are moving between the body’s blood vessels and tissues, constantly adjusting the parameters as new measurements come in. Our proprietary adaptive controller then adjusts the fluid-infusion settings accordingly. One advantage of our technology is its attention to what control engineers call closed-loop system stability, which means that any perturbations to a normal state lead to only small and f leeting variations. Many engineering applications use control systems that guarantee closed-loop stability—when an airplane runs into powerful turbulence, for instance, the autopilot system compensates to keep the shaking to a minimum. However, most control systems for medical devices have no such guarantee. If doctors judge that a sepsis patient’s fluid levels have dramatically dropped, they might push a large volume of fluid into the bloodstream, perhaps overcompensating. We’ve already tested our automated fluid-management system in collaboration with William Muir, a veterinary anes- thesiologist and cardiovascular physiologist. Working with dogs that were experiencing hemorrhages, we used our system to regulate their fluid infusions. Our system successfully kept the dogs in stable condition as measured by the volume of blood pumped with every heartbeat. We need to do more testing in order to win regulatory approval for a fully automated fluid-management system for humans. As with our work on ventilator management, we can start by building a decision support system for the ICU. This “human in the loop” system will present information and recommendations to the clinician, who will then adjust the settings of the infusion pump accordingly. L O O K I N G B E YO N D V E N T I L AT I O N and fluid management, other key aspects of patient care that could be automated include pain management and sedation. In the ICU of the future, we envision many such clinical operations being monitored, coordinated, and controlled by AI systems that assess each patient’s physiological state and adjust equipment settings in real time. To make this vision a reality, though, it won’t be enough for engineers to produce reliable technology. We must also find our way through many regulatory barriers and institutional requirements at hospitals. Clearly, regulators need to scrutinize any new autonomous medical system. We suggest that regulatory agencies make use of two testing frameworks commonly used in the automotive and aerospace industries. The first is in silico trials, which test an algorithm through computer simulations. These tests are useful only if the simulations are based on high-fidelity physiological models, but in certain applications this is already possible. For example, the U.S. Food and Drug Administration recently approved the use of in silico testing as a replacement for animal testing in efforts to develop an artificial pancreas for diabetics. The second useful framework is hardware-in-the-loop testing, where hardware stands in for the object of interest, whether it’s a jet engine or the human circulatory system. You can then test a device—an autonomous fluid pump, say—on the hardware platform, which will generate the same type of data you’d see on an actual patient’s bedside monitor. These hardware-in-the-loop trials can show that the device performs well in real time and in the real world. Once these technologies have been proven with stand-ins for critically ill humans, testing can begin with real patients. To bring these technologies into hospitals, the final step is to win the trust of the medical community. Medicine is a generally conservative environment—and for good reason. No one wants to make changes that might threaten the health of patients. Our approach is to prove our technologies in stages: We’ll first commercialize decision-support systems to demonstrate their efficacy and benefits, and then move to truly autonomous systems. With the addition of AI, we believe ICUs can be smarter, safer, and healthier places. n ↗ POST YOUR COMMENTS at https://spectrum.ieee.org/smarticu1018 SPECTRUM.IEEE.ORG | NORTH AMERICAN | OCT 2018 | 35
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