Observations & Opportunities: Nanoscale Challenges As Chipmakers Produce ICs with 10nm & 7nm Features

Remember the sub-micron-featured ICs in PCs just a few decades ago? That was doable. Some chipmakers did complain a bit about technical challenges as they retooled for smaller-featured optical lithography requirements (180nm, 90 nm, 65 nm, 45 nm, 32 nm, etc.) and more demanding processing but they all knew they could make it work, profitably. With 10nm or smaller, however, profitability is and manufacturing are more challenging technology leaps. This Blog explores some emerging IC and MEMS manufacturing advances and challenges.

The Bleeding Edge
The world’s most demanding and sophisticated high-volume thin-film deposition occurs during the wafer fabrication of nano-scaled ICs and some MEMS devices. MEMS (Micro-Electro-Mechanical Systems) typically are a few generations behind ICs smaller critical dimensions but the MEMS devices are increasingly used in mission-critical applications which demands precision and repeatability. MEMS must provide that requisite physical interface to the world on micro and nano scales. Since MEMS incorporate electrical and/or optical I/Os and/or other electronic circuitry, this obviously impacts design and manufacturing efforts.

Remember the “Hype Cycle for Emerging Technologies, 2017” from Gartner Inc.[1]. Here it is again below. Consider that virtually none of these anticipated high-growth market opportunities will be possible without more computing power in ever smaller packages and that increasingly includes AI (Artificial Intelligence) to enable previously unattainable results. To get this performance, chipmakers and computer makers need faster processors and more memory, the latter preferably being fast and rugged. One given: vacuum-enabled thin-film processing for deposition, plasma etching and cleaning demands will be tougher than ever.

The hype cycle for emerging technologies. Source: Gartner Inc., Stamford, CT


We’ve all heard about the options being evaluated when silicon ICs can no longer do the job. Nanosheets and carbon nanotube transistors are possibilities but this is an industry that usually resists change in its wafer fabrication processes. That does not mean they won’t do it if necessary. There is also the requisite support infrastructure needed to maintain and tweak process machines for ≤10nm production.

Parallel Production
It will take years to transition from mainstream IC manufacturing to EUV lithography and even longer if non-silicon devices are used. But ≤10 nm devices will not lessen the need for currently maxed out existing optical lithography wafer fabs that will likely keep producing “standard” ICs and MEMS for decades since those devices will be needed for mainstream applications. Recently, more 3D deices with many layers help cram more into chips.

The emerging ≤ 10nm generation is beyond most chipmaker’s comfort zones. We’ll explore some of the challenges these semiconductor manufacturers face as early production of chips with feature sizes at 10nm and 7nm begins, perhaps using EUV lithography, if it is ready, or pushing forward with older but proven familiar lithography technology awaiting EUV reality with high wafer-per-hour throughputs.

Now a nomenclature caution: chipmakers are being creative with marketing terminology when it comes to exact “nodes” that they are actually using now, what is planned and with 10nm or 7nm claims are not being truly comparable. “Node” being a somewhat vague way of describing “industry standard” references to the geometries being fabricated. The bottom line: feature sizes are shrinking one way or another so producing more transistors per mm2 is a given.

Mark Bohr is an Intel Senior Fellow and director of process architecture and integration at Intel Corporation. During the recent Technology and Manufacturing Day presentations, pointed out Intel’s innovation enabled technology pipeline carries through to 7nm, 5nm and 3nm feature sizes.

Intel’s innovation enabled technology pipeline.


Clearing Up the Node Naming Mess
Bohr posits, “Let’s clear up the node naming mess. Moore’s Law, as stated by our co-founder over half a century ago, refers to a doubling of transistors on a chip with each process generation. Historically, the industry has been following this law, and has named each successive process node approximately 0.7 times smaller than the previous one – a linear scaling that implies a doubling of density. Thus, there was 90 nm, 65 nm, 45 nm, 32 nm – each enabling the packing of twice the number of transistors in a given area than was possible with the previous node.

“But recently – perhaps because of the increasing difficulty of further scaling – some companies have abandoned this rule, yet continue to advance node names, even in cases where there was minimal or no density increase. The result is that node names have become a poor indicator of where a process stands on the Moore’s Law curve.”

“Every chip maker, when referring to a process node, should disclose its logic transistor density in units of MTr/mm2 (millions of transistors per square millimeter) as measured by this simple formula. Reverse engineering firms can readily verify the data.

Intel Transistor Density Formula


“There is one important measure missing: SRAM cell size. Given the wide variety of SRAM-to-logic ratios in different chips, it is best to report SRAM cell size separately, next to the NAND+SFF density metric.

“By adopting these metrics, the industry can clear up the node naming confusion and focus on driving Moore’s Law forward,” added Bohr.

Intel’s 10nm
Before the Consumer Electronics Show (CES 2017) began, Intel Corp. kicked off the event with announcements in automated driving, 5G technologies and virtual reality — or the more advanced merged reality.

“The pace of technology improvement is accelerating faster than ever,” said Intel CEO Brian Krzanich. “Moore’s Law is at the center of this acceleration. Technology is extending far beyond consumer electronics, defining almost every aspect of our lives, and transforming industries.”

Krzanich described the company’s vision for VR and merged reality technology and how these technologies will reinvent the experiences of travel, work safety and productivity, and sports and gaming. He explained that Intel’s first 10nm-powered 2 in 1 PC with Intel’s next generation processor, codenamed Cannon Lake, made it possible. Intel’s Cannon Lake 10nm technology is said to have the world’s tightest transistor and metal pitches, created with hyper scaling, for the highest density in the industry.

Intel’s Cannon Lake 10nm technology is said to have the world’s tightest transistor and metal pitches


During Intel’s Technology and Manufacturing Day last year, Kaizad Mistry, Corporate Vice President, Technology and Manufacturing and Co-Director, Logic Development, said that Intel’s 10nm technology is a full generation ahead of other “10nm” processes. Mistry added, “Hyper scaling extracts the full value of multi-patterning schemes and and allows Intel to continue the benefits of Moore’s Law economics.”

2018—The Year of 10nm Critical Dimensions and EUV Lithography
From several published reports over the past year, 2018 is the year of ≤10nm with EUV (Extreme UltraViolet) but we’ve heard that before.

There are just a handful of chipmakers now who can afford the new 10nm, 7nm and smaller lithography equipment. Taiwan Semiconductor Manufacturing Company (TSMC) is challenging Samsung Electronics in the race for ≤7nm process volume production in 2018. TSMC has many orders for fabricating chips with 7nm processing for mobile communications, high-performance computing and AI (artificial intelligence) applications. Apple and Qualcomm are among TSMC’s major customers, with the foundry house contracted to fabricate all the advanced Apple A12 processor chips for the 2018 next-generation iPhones. TSMC will use extreme ultraviolet (EUV) technology for its 7nm+ process and step up EUV deployments in the 5nm and 3nm processes. TSMC will outpace Samsung in 7nm volume production in 2018.

TSMC is building a 5nm production fab (Fab 18) in the Southern Taiwan Science Park this year. It will also invest US$20 billion on a 3nm fab in the park, with construction starting in 2020. Their 5nm process will be an extension of its 7nm process, targeting mobile communication, high-performance computing, AI, and machine learning (ML) applications.

Samsung is building a 7nm production line in Hwaseong, Korea. The company is negotiating with U.S. and Chinese customers over new projects. Samsung spun off its wafer foundry as an independent business unit in 2017. It will launch 4nm processing in 2020 against TSMC’s 5nm node, before starting volume production of 7nm process in 2018 and developing 6nm and 5nm processes in 2019, according to sources.

Samsung says it will start manufacturing chips with circuitry widths of 7nm by using EUV tech in the second half of 2018. TSMC also said hat its chip manufacturing process using EUV technology will be the “most advanced technology in foundry industry” in 2018 in terms of density, performance and power.

Meanwhile, GLOBALFOUNDRIES’ new 7LP 7nm FinFET process technology targets high-performance, power-efficient System-on-Chips (SoCs) for demanding, high-volume applications. This provides world-class performance, power, area and cost advantages through 7nm scaling. Based on a 3D FinFET transistor architecture imaged with optical lithography but it is EUV compatible where needed. 7LP delivers more than twice the logic and SRAM density, and either >40% performance boost or >60% total power reduction, compared to 14nm foundry FinFET offerings.

GLOBALFOUNDRIES 7LP 7nm FinFET process technology platform produces high-performance, power-efficient SoCs in demanding, high-volume applications


With 100 million transistors per square millimeter density, or more, being normal now, the world’s most demanding and sophisticated high-volume thin-film deposition requirements during wafer fabrication of ICs and MEMS devices only get more demanding. Wafer fabrication toolmakers must continue to refine their machines, perhaps even including AI at some point to handle new data requirements and process variations needed by customers. With the high stakes for all involved, every process must be exceedingly accurate and repeatable.

1. Terrence Thompson, Vacuum Technology & Coating, “Faster Changes and the Implications” blog, August 2017, posted on Aug. 23, 2017, Observations & Opportunities.
2. Steve Hansen, Vacuum Technology & Coating, Vol. 18, No. 2, Feb. 2017, “Miniature Analyzers and IonTraps From Compact to MEMS”, pp. 12-16.
3. Donald M. Mattox, Vacuum Technology & Coating, Vol. 5, No. 12, Dec. 2004, SVC Educational Guide, “Applications: Low-Pressure CVD and PECVD: Plasma-Enhanced Chemical Vapor Deposition (PECVD)”, pp. 28-30.

Note: The many vacuum-centric deposition, coating, etching and cleaning processes that Vacuum Technology & Coating has covered recently, as well as those going back almost 15 years, are worth exploring. These articles serve as invaluable basic primers while others describe leading-edge applications. These are free articles, and magazine issues, to peruse and educate. Visit http://www.vtcmag.com and explore the magazine’s online viewing or downloadable PDF issues. Use the Search button to find specific topics.

Next time: We’ll take a look at some new MEMS sensors and why they are essential.

Automaking 2.0

If automakers, and those that follow the industry closely are correct, we won’t recognize the industry, or the vehicles either, in 20 years. For the auto industry, 20 years is just around the corner and investments in new technologies are being made now. Our future mobility ecosystem specifics are changing dramatically with safety and environmental concerns for both vehicles and the road infrastructures. For many observers, it seems like a science fiction movie that has become all too real, too quickly. To those that grew up when driving was still fun at times, it will be a very strange new world indeed.

AI-enabled and controlled AV and EV technology is redefining how the global automobile manufacturing industry and its suppliers build tomorrow’s vehicles. Manufacturing innovations underway now will reshape the supply chains and alter vehicle manufacturing landscapes. The traditional internal combustion engine and its drive train will be replaced in many vehicles by electric motors and batteries or fuel cells. There are still unanswered questions about the global environmental impact of the requisite electric-powered vehicles to pull this off but that is not garnering as much attention yet.

Examining some possibilities and concerns from automakers and tech entrepreneurs provides additional AI (Artificial Intelligence), AV (Autonomous Vehicles) and EV (Electric Vehicle) insights. One underlying issue is the potential demand by vehicle function and ownership: who actually purchases them? As AV use ramps up, will these be privately owned vehicles, like today’s cars, leased or just an Uber or Lyft shared ride? What about AV trucks or other delivery vehicles?

DHL has teamed with Ford to build a midsize e-van called the StreetScooter Work XL. Each vehicle is custom-outfitted with shelves that can carry more than 200 packages. It can be loaded through the tailgate or curbside door. The vans can travel 50 to 125 miles on a charge.

Within a decade, the interior of AVs will likely change to accommodate occupants freed from the tedious tasks of monitoring road and traffic conditions and driving itself. Interconnected passengers will now be able to relax, perhaps enjoy some entertainment, or use their phone, tablet and/or laptop PC to work in the very mobile AV environment. Some vehicles could become offices on wheels. Relaxed and/or productive time instead of road rage would certainly be welcome for many passengers.

Initially, the strongest demand will likely be for privately-owned AVs with luxury cars leading the way. For some time, certain individuals will want their own vehicle even if it is not necessary. There are plans for very affordable EVs too, both battery powered and hybrid, with fully AV models in the works. Fuel cells are still a possibility too. But, as with other automobile innovations over the years, the technology will soon permeate the full range of vehicles with economies of scale moderating costs.

Historically, automakers try to save pennies with every component so cost is always an issue. However, integrating all the sophisticated electronics, computing power, sensors and actuators required for AVs, and the need for all of these things to be as light and as small as possible while being unobtrusive, automakers will need the latest semiconductor IC and MEMS magic to seamlessly integrate functionality and reliability into their onboard Operating System (oOS anyone?).

Will building AVs be expensive? Yes, especially in the early versions as real world feedback and experience will likely necessitate many ongoing changes to fit the evolving infrastructure.

Fewer Individually-Owned Automobiles?

It seems that virtually everything is changing in the automotive universe with AVs and EVs increasingly becoming the norm in the next few decades as internal combustion engines are phased out and/or outright banned for most some uses. As described in the previous Blog, December 6, 2017, “Autonomous Vehicles (AVs) Powered by AI—What Could Possibly Go Wrong?” Today, the bellwether General Motors is “all in” with the AV and EV businesses going forward.

“General Motors is investing heavily in ride-sharing, electric vehicles, autonomy and other mobility services in order to put customers and consumers first, and dealers will have to adjust,” G.M. CEO Mary Barra says. “The transformative technologies—electric vehicles, autonomous vehicles—provide an opportunity to grow, again, where it makes good business sense. If you look at where ride-sharing is the most popular, it’s in dense, urban environments where we have low market share, so that’s where we see it as additive,” Barra said. “I do think that we’re going to be in the core business for a very, very long time, but we’re going to continue to be led by the customer.”

Will Carmakers, As We Know Them, Survive?

A fascinating read for those automobile enthusiasts, or those involved as suppliers to the auto industry, is detailed in a special report entitled Redesigning the Industry. [1] “A new auto industry is forming, triggered by a reimagining of the vehicle itself. New business models are coalescing, centered on technology innovators, fleet operators, services businesses and platform providers.

“Emerging technology will ultimately lead to fully self-driving vehicles. Mass-market electrified vehicles also are gaining ground rapidly. So-called connected cars and mobility services such as onboard navigation and entertainment are redefining the industry. As management consulting firm McKinsey and Co. puts it, the auto industry “is ripe for disruption.””

Unsettlingly thought this may be to some, this disruption is being implemented within the auto industry now. Recent G.M. descriptions of where it is headed suggests that its main business may not be always focused on building automobiles for individual customers but producing fleets of AV taxi cabs or other special-purpose AV and EV vehicles. Ten years ago, few would have even thought this possible.

“We are approaching the end of the line for the automobile.”

The Auto News report quotes Bob Lutz, ex-vice chairman of General Motors who bluntly states, “We are approaching the end of the line for the automobile.” Lutz adds, “Within 20 years the human-driven automobile, its repair facilities, its dealerships … will be gone. Auto brands will be no longer named Chevrolet, Ford or Toyota,” he writes, but rather “Uber or Lyft or whoever else is competing in the market.”

If manufacturing vehicles for individual ownership and operation then becomes a sideline to be the automakers’ main goal, what is needed in Autonomous Vehicle (AV) technology to make it more affordable, safer and environmentally friendly? Much more affordable and reliable electronics, sensors and batteries are part of the answer. Will it be military-grade fail-safe technology or just better technology than what is in most new cars now? Maybe one of the PC companies will just offer a customizable vehicle OS and end the discussion.

AV and EV Adoption Rates

There is considerable hyperbole about the rate at which AVs and EVs can be produced and when they will really become a sizable portion of the vehicles on the roads. There also are concerns about how producing the electricity needed to build the vehicles and to operate them is adding to carbon emissions.

One recent IEEE Spectrum report entitled Electric Vehicles Aren’t Taking Over Our Roads as Fast as Hype Artists Claim stated that “Unrealistic forecasts have been the norm. In 2008, Deutsche Bank predicted that EVs would claim 7 percent of the U.S. market by 2016; in 2010, Bloomberg Businessweek put the 2016 share at 6 percent. But actual sales came to 158,614 units, just 0.9 percent of the record 17.55 million vehicles sold that year.” Clearly, observers overestimated the rate of implementation. But with the announced and current production levels, there will be more EVs on the road in the next few years.

The author, Vaclav Smil, added, “If EVs are to reduce carbon emissions (and thus minimize the extent of global warming), their batteries must not be charged with electricity generated from the combustion of fossil fuels. But in 2016, 68 percent of global electricity originated in fossil fuels; 5.2 percent came from wind and solar and the rest from hydro energy and nuclear fission.”

An excellent point that is being addressed now with the strong growth of renewables including solar panels and wind turbines for power companies, businesses and individuals. This allows some some EV owners to charge from their solar panels at home—off the grid.

The demand for urban commercial transport is rising—and so are the consequences.
Source: McKinsey & Company

A recent McKinsey & Company report Urban commercial transport and the future of mobility stated, “More than half of the world’s population lives in cities, and more people move to the concrete jungle every day. [2] By 2045, United Nations data crunchers project, 6 billion–plus of us will reside at urban addresses, lured there for personal and professional reasons.

“That influx will strain transit infrastructure far beyond its original road maps.

“Cities are the heart of the global economy, accounting for more than 80 percent of world GDP. Roads, rails, and other forms of transportation are the arteries that nourish that heart. When these become clogged or weakened, the results are severe.”

McKinsey also states that night deliveries can shift traffic to off-peak hours; reduce congestion during the day and allow suppliers to use bigger trucks, reducing the number of deliveries. In dense, developed cities, shifting to night deliveries could speed up commercial deliveries by half and cut costs by up to 50 percent. “For all the potential, though, the use of night deliveries in cities is limited, largely because of noise concerns; eventually, the use of EVs could help because they are quieter—and would also sharply reduce related emissions.”

Other delivery approaches look particularly promising: EVs, load-pooling, parcel lockers, and autonomous ground vehicles (AGVs). McKinsey & Company estimates that these six solutions could reduce tailpipe emissions by up to 30 percent (and eliminate them altogether through electrification) while also cutting costs per parcel by 25 to 55 percent.

Going Forward

For those using vacuum-centric processes, this all out push to transform the auto industry is very positive. More semiconductor integrated circuits (ICs) and MEMS sensors and actuators will be needed and they need vacuum-centric technology to make it happen. These technologies are essential for the new generations of batteries being developed as well as innovative fuel cells.

For Next-Gen IC and MEMS fabrication, what nanotech deposition, etching and cleaning processes will win out? Good question. Even as chipmakers struggle to validate and/or begin initial single-digit nanometer EUV lithography, they may think that they have most of the processes pinned down but advanced high-volume production implementation always has some surprises.


1. Redesigning the Industry, an Automotive News Special Report, Crain Communications Inc.
2. Report – September 2017, Urban commercial transport and the future of mobility, McKinsey & Company

Note: The many vacuum-centric deposition, coating, etching and cleaning processes that Vacuum Technology & Coating has covered recently, and going back almost 15 years, are worth exploring. Some articles serve as invaluable primers while others describe leading-edge applications. These are free articles, and issues, to peruse and educate. Visit http://www.vtcmag.com and explore the magazine’s online or download PDF issues. Use the Search button to find specific topics.

Next time: Remember sub-micron? The next Blog explores more of the nano challenges as semiconductor manufacturers begin producing chips with feature sizes smaller than 10 microns using EUV lithography. The race to these smaller IC critical dimensions also impacts MEMS device manufacturing with their shrinking dimensions too. The world’s most demanding and sophisticated high-volume thin film deposition occurs during the wafer fabrication (manufacture) of ICs and MEMS devices. MEMS may be a generation or more behind the IC makers in regard to critical dimensions but the devices are increasingly used in mission-critical applications which demands precision and repeatability. MEMS provide that requisite physical world interface on micro and nano scales. This obviously impacts design and manufacturing efforts. The next Blog explores IC and MEMS manufacturing advances.


Observations & Opportunities: Autonomous Vehicles (AVs) Powered by AI—What Could Possibly Go Wrong?

This time, the AV discussion continues with some general and specific Artificial Intelligence (AI also known as Machine Intelligence, MI) downsides, challenges and tips. Autonomy, safety and efficiency are obvious AI goals for AVs. AI is a tool ranging from simple to complex. If AI is implemented with a thorough understanding of the specific goals to accomplish, it may work very well and exceed expectations. If not, watch the headlines.

AI is the fast-evolving foundation for many new technology efforts now including self-driving cars, a.k.a., AVs. There are as many different approaches to AI for AVs as there are companies working on them and the AVs themselves. Automakers, with their own systems, or those that are evaluating the alternatives from Waymo by Google (Alphabet), Apple, or others, will cull the field at some point. Some systems will be too expensive and others may rely upon unproven technologies that will turn off auto manufacturers. Some standardization is necessary too for regulation and insurance purposes. AV appearance will be a factor since some of the roof-mounted sensor arrays are just plain ugly and need cloaking devices. Some of the non-aerodynamic sensor arrays on roofs would increase noise and decrease mileage.

Waymo’s Chrysler Pacifica Hybrid minivan AV testbed

As Waymo points out, the world had 1.25 million deaths worldwide due to vehicle crashes in 2014. There were 32,675 deaths in the U.S. alone due to vehicle crashes in 2014. And there was a 6% increase in traffic fatalities in 2016, reaching the highest point in nearly a decade. The primary cause: “There’s a clear theme to the vast majority of these incidents: human error and inattention.” Safety is a primary Waymo goal.

Apple’s AI for AV efforts are usually somewhat secret. However, they recently posted their Apple Machine Learning Journal online. It opens with “Welcome to the Apple Machine Learning Journal. Here, you can read posts written by Apple engineers about their work using machine learning technologies to help build innovative products for millions of people around the world. If you’re a machine learning researcher or student, an engineer or developer, we’d love to hear your questions and feedback.” It provides some insights on what they are doing.

How much AI is used in products or services including AVs varies enormously but it is the elephant in the room for all “smart” products whether it be your smartphone, tablet, car or thermostat. Like them or not, AI-enabled products and services can turn up anywhere and there will be some inevitable marketing hyperbole about new “AI” products. AI-enabled things may be smarter than they were but they still lack common sense and often adequate security.

A recent publicized fender-bender accident involving a newly deployed AV bus suggests that an AV should have enough latitude to back up, if no one is endangered, when an obvious threat is posed by another vehicle backing up towards it. Of course, some human drivers would have not taken evasive action either so blaming AI for lack of foresight or adequate programming is really blaming humans too.

AI is, and likely will be for some time, a blend of hardwired programming for following rules of the road as well as having a ML (machine learning) capability to avoid repeating mistakes and learning how to perform better. There certainly are differing approaches for an AV’s operating system (OS) depending, in part, on whether it was conceived by an old-line auto company or an new entry such as Apple or Waymo (Alphabet/Google).

How Extensive Is AV and AI?

One insightful recent report proclaimed, “There are basically three big questions about artificial intelligence and its impact on the economy: What can it do? Where is it headed? And how fast will it spread?

“Three new reports combine to suggest these answers: It can probably do less right now than you think. But it will eventually do more than you probably think, in more places than you probably think, and will probably evolve faster than powerful technologies have in the past.” This suggests that robust global AI efforts are not only underway but gaining momentum.

As a disclaimer, most of the companies describing AI advancements now do so relative to their own efforts. A dispassionate comprehensive overview of AI can be found online by visiting the Artificial Intelligence Index and reading their AI Index 2017 Annual Report. For those considering or upgrading AI in their products or services, the report is very revealing and allows one to avoid some common pitfalls.

AI, AV and EV Futures: Defined by the Giants

In AI, the really large technology leaders and their competitors are battling it out in the AI marketplace. It is a battle where substantial financial resources and engineering talent help carve out significant market shares. So, are these large corporations too influential?

“We are experiencing a time, where five companies are holding most of the economical (and even political) power in the world: Facebook, Alphabet, Amazon, Apple and Microsoft” surfaced in a recent blog. Alphabet (Google and Waymo), Tesla (SpaceX and the Hyperloop Pod transport-in-a-vacuum system), Uber and Apple are certainly active in AI for AVs.

Some AI efforts may require more than what those companies alone can accomplish alone or with a few partners. If one has any doubt that AI, or AI in AVs, is real, just check out the news and recent technical papers presented by these companies and their automobile manufacturing competitors around the world. Of course, the automobile manufacturers are very active which is no surprise. Don’t overlook the universities and R&D centers are doing AI and AV research.

Many of the biggest companies involved with AI projects had modest efforts until the past few years, the exception being Waymo. AI and its impact on AVs, EVs (Electric Vehicles) and other products and services were modest but with some very promising results. Some of the deep-pocket companies were busy with projects that were not yet public knowledge. However, with AVs, General Motors just announced that they are “all in” with the AV and EV businesses going forward.

G.M. produced their first round of AV self-driving Chevrolet Bolt cars at their GM Orion Assembly plant in Orion Township, Michigan. G.M.’s president, Daniel Ammann, told journalists that the cars would be ready for consumer applications in “quarters, not years.”

G.M. also plans to build a driverless ride-hailing fleet by 2019 that may eventually become part of the automaker’s core business. [G.M. the taxi company?] Executives told investors that by 2025, AV cost reductions and increased consumer purchases should combine and drive prices down to less than $1/mile, or about a third of current ride-hailing prices.

G.M.’s Chevrolet Bolt EV is the first U.S.-made, mass-market, fully electric car

“We’re aiming for a future of zero crashes, zero emissions and zero congestion,” G.M. CEO Mary Barra said.

Aside from the AV future, G.M. executives said the company sees a clear path to profitability through a wide array of EV electric cars, which so far have yet to take hold with consumers due to a combination of cost and range anxiety but the latter may be reduced as battery technology is improving.

Innovations Welcome

The current breakthroughs in commercializing AI, AVs, EVs and related technologies will provide opportunities for those with innovative ideas. With some companies now offering do-it-yourself (DIY) ML (Machine Learning) apps, it may get much more interesting quickly.

The AI genie is out of the bottle. Governments, corporations, schools and individuals certainly have increasing access to AI technologies. One major question is whether or not they will make informed decisions regarding selections, implementations and usage. The general public does not have an in-depth knowledge of what is being planned or secretly deployed whether it be for the greater good or for nefarious purposes. However, the better that AI is understood by everyone, the more likely consensus positive outcomes will be implemented.

It Is AV Rocket Science

Yes, space launch rocket systems can be AVs too. Elon Musk, of Tesla automotive fame, sees his impressive SpaceX space launch venture on a roll. Their satellite launches in 2017, to date, are impressive and are leading the way to more affordable launches among global space launch providers. It’s nice to see a private company succeed in an area long dominated by government or existing aerospace companies. Do reusable SpaceX rockets use AI? Yes. In fact, AI was the tool that allowed analysis of the key engineering challenges that make landing huge rockets possible.

No entity had seriously studied re-entry and landing the first stages of the rockets until recently. Companies and governments had just figured disposable rockets were the cost of doing business since everyone used them. Even the now defunct U.S. Space Shuttle’s original reusability goals faded over the years for numerous reasons.

A Falcon 9 first stage successfully landed on SpaceX’s autonomous spaceport drone ship in the Atlantic Ocean. This first stage was reflown in March 2017, the first-ever reflight of an orbital-class rocket stage.

Musk is certainly not alone in the private space launch business, other tech billionaires including Paul Allen [co-founded Microsoft with Bill Gates, and founder of the Allen Institute for Artificial Intelligence and Stratolaunch Systems, a space transportation venture using an air launch to orbit system] and Jeff Bezos (Amazon and Blue Origin spacecraft founder) may be enabling the new age in space launch efficiency and affordability. These new “kids” are so good that even the old Russian standby Soyuz team is scrambling to rework their business to compete.

The Soyuz spacecraft was designed for the Soviet space program by the Korolev Design Bureau (now RKK Energia) in the 1960s. The Soyuz was originally part of the unrealized Soviet manned lunar manned landing program. The Soyuz capsule launches on a Soyuz rocket, a frequently used and very reliable launch vehicle. The Soyuz rocket is based on the early Vostok launcher, which in turn was based on the 8K74 (R-7A Semyorka) Soviet-era ICBM.

According to one recent report, “This year [2017] has seen a number of firsts for the [SpaceX]—first reflight of a Falcon 9 booster, first reuse of a Dragon cargo spacecraft, first national security payload, and a remarkable dozen landings. But probably the biggest achievement has been finally delivering on the promise of a high flight rate.”

Yes, these entrepreneurs are using AI to fine-tune their space transportation operations as well as their other commercial enterprises. Yes, AI works.

Using AI for AVs & Everything Else

Although science and engineering innovations are built upon the accomplishments of the past, fresh eyes from younger visionaries and the curious may shake up industry and business going forward. When the the young ask “Why?”, or see something that looks overly complicated, fresh insights may lead to greater efficiency and productivity.

Musk is both a user and promoter of AI. “Since its founding by Elon Musk and others nearly two years ago, nonprofit research lab OpenAI has published dozens of research papers. One posted online … is very different: Its lead author is still in high school.

“The wunderkind is Kevin Frans, a senior currently working on his college applications. He trained his first neural net—the kind of system that tech giants use to recognize your voice or face—two years ago, at the age of 15. Inspired by reports of software mastering Atari games and the board game Go, he has since been reading research papers and building pieces of what they described. “I like how you can get computers to do things that previously you would think were impossible.”

Frans was working on a tricky problem that was holding back robots and other AI ML systems, i..e., “How can machines tap what they’ve previously learned to solve new problems?”

Humans do this instinctively but machine-learning (ML) software typically repeats its lengthy training process for every new problem—even when there are common elements.

Frans’s paper, with four others affiliated with the University of California Berkeley, reports progress on this problem. “If it could get solved it could be a really big deal for robotics but also other elements of AI,” Frans says. He developed an algorithm that helped virtual legged robots learn which limb movements could be applied to multiple tasks, such as walking and crawling. In tests, it helped virtual robots with two and four legs adapt to new tasks, including navigating mazes, more quickly. Fresh ideas help.

Next time: AI-driven AV technology will certainly reshape the global automobile manufacturing industry and its suppliers. Manufacturing innovations are likely that will reshape the supply chains and manufacturing landscape. A look at some possibilities and additional concerns from both automakers and tech entrepreneurs will provide additional AI and AV insights.

Observations & Opportunities: Self-Driving Vehicles—Challenges Abound

Remember when you used the rearview mirror to back into a parking space? Some of us still do but many vehicles now include, or provide as an option, automated parking driving aids. In practice, it conveniently works with another system that avoids backing over unsuspecting pedestrians or hitting inanimate objects (the increasingly common AI-based automated braking systems).

This is just one example of what basic driver-assistance systems can do now. Yes, some consumers are still skeptical of fully AVs (autonomous vehicles)/ADS/ADAS (advanced driver assistance systems). They question their practicality but also like the idea of being able to do other things to pass the time while stalled in traffic congestion. The many heavyweight companies developing AVs are counting on the latter. The expected decrease in traffic accidents and minimized congestion are benefits that insurance companies and governments applaud.

To counter the increasingly common dashboard infotainment systems, and all the mobile devices including laptops for consuming media and information brought onboard, and seemingly ubiquitous smartphones, that dangerously distract drivers now, AVs that could safely transport distracted occupants should become more popular and better for all concerned.

Perhaps the practical limits of AV system implementation are “driver” and passenger sensory overload plus the thought of simply not being in control. Also, significant AV system costs and technology maturity are concerns. Even when AV systems become commonplace, the cost of the components alone will be substantial. As Tesla Inc. indicated recently, the cost differential between assisted driving and full AV capabilities is several thousand dollars. What all of these various systems use is dependent, to varying degrees, upon affordable and reliable artificial intelligence (AI) implementation with associated sensors and actuators. [Also see earlier blogs on AI & Virtual Reality—Let The Games (and Work) Begin and AI & Autonomous Vehicles—Awesome Implications]

Some AV Reality Checks

At this point in time, it would be difficult to find an automobile manufacturer that is not actively working on integrating sophisticated autonomous vehicle operation in some, or all, of its vehicles. At the same time, these manufacturers are increasingly focusing on electric vehicles (EVs) or hybrid gasoline-electric vehicles. In a decade, internal-combustion engine vehicles will be disappearing.

Since many “technology” companies regard the automobile industry as lacking in computer, microelectronics and software expertise, the established computer hardware and software companies are either developing AV systems independently or in joint ventures with familiar automotive companies. Alphabet’s Google, Apple, Intel, Microsoft, and others are very active with massive ongoing investments. Recently, NVIDIA Corp. has been releasing AI chips specifically for AV applications with impressive early evaluations. Realistically, not all will be successfully adopted but differing AV systems will eventually enter mass production.

General Motors Co. recently acquired the LIDAR technology company Strobe, Inc. As part of the deal, Strobe’s engineering talent joins GM’s Cruise Automation team to define and develop next-generation LIDAR solutions for self-driving vehicles. GM thinks its AV team is complete now.

Lidar (also called LIDAR, LiDAR, and LADAR) originally was a surveying technology that measured distance by illuminating a target with a pulsed laser light and measuring the reflected pulses with a sensor. Differences in laser return times and wavelengths made digital 3D-representations of the target.

LIDAR is said to create higher-resolution images that provide more accurate views of the world than cameras or radar alone. As self-driving technology continues to evolve, LIDAR’s accuracy may play a critical role in its deployment.

“The successful deployment of self-driving vehicles will be highly dependent on the availability of LIDAR sensors,” said Julie Schoenfeld, Founder and CEO, Strobe, Inc.

GM is planning to test its vehicles in “fully autonomous mode” in New York state in early 2018, according to New York Governor Andrew Cuomo. However, the planned testing by GM and its self-driving unit, Cruise Automation, will initially be a Level 4 autonomous vehicle.

A level 3 car still needs a steering wheel and a driver who can take over if the car encounters a problem, while level 4 promises driverless features in dedicated lanes. A level 5 vehicle is capable of navigating roads without any driver input and in its purest form would have no steering wheel or brake pedal.

General Motors’ Cruise Automation LIDAR on a Chevrolet Bolt EV (electric vehicle)



The many AV systems use very sophisticated sensors to evaluate the immediate surroundings that vehicle owners, or automobile mechanics, may not fully understand. Looking at AV going forward, the mainstream vacuum-dependent deposition and etching/cleaning processes will likely need refinements and evolution. Unlike a PC or smartphone problem where failure may be annoying, if a driverless vehicle goes awry because of a microprocessor or sensor component manufacturing defect, the consequences can be tragic and very expensive.

Those using the ICs, MEMS and other micro devices for AI and AV have at least two options to minimize liability, build in considerable redundancy or use fail-safe components and systems. With IC manufacturing, although each device on a wafer may be very similar, they are not absolutely identical. That means that the patterning lithography must be nearly perfect and each layer of material deposited must be incredibly uniform. What was acceptable yesterday may not be good enough going forward.

AI, in the most basic terms, suggests that its (dedicated) computer is cognizant of the specified environment (using sensors) and performs tasks within its defined decision-making capabilities (using actuators). In a vehicle, AV control is tied into an already extremely complex electrical/electronic system with its own central computer and with dedicated embedded computers within specific components—a complex network.

“Artificial intelligence (AI, also machine intelligence, MI) is apparently intelligent behavior by machines, rather than the natural intelligence (NI) of humans and other animals. In computer science AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving” per Wikipedia.

“Intelligent agents are often described schematically as an abstract functional system similar to a computer program. For this reason, intelligent agents are sometimes called abstract intelligent agents (AIA) to distinguish them from their real world implementations as computer systems, biological systems, or organizations.”

Simple reflex agent, Utkarshraj Atmaram, Wikipedia


A Broader Perspective of AI and AV

Microelectronics technology is invading many products and usually are interconnected via the Internet. If one looks at a self-driving car, i.e., will the passengers be viewing live TV?

Recently, Gartner, Inc., the Stamford, CT technology research company, highlighted the top strategic technology trends that will impact most organizations in 2018. Creating systems that learn, adapt and potentially act autonomously will be a major battleground for technology vendors through at least 2020. The ability to use AI to enhance decision making, reinvent business models and ecosystems, and remake the customer experience will drive the payoff for digital initiatives through 2025.

“Currently, the use of autonomous vehicles in controlled settings (for example, in farming and mining) is a rapidly growing area of intelligent things. We are likely to see examples of autonomous vehicles on limited, well-defined and controlled roadways by 2022, but general use of autonomous cars will likely require a person in the driver’s seat in case the technology should unexpectedly fail,” said David Cearley, vice president and Gartner Fellow in his Top 10 Strategic Technology Trends for 2018 presentation. “For at least the next five years, we expect that semiautonomous scenarios requiring a driver will dominate. During this time, manufacturers will test the technology more rigorously, and the nontechnology issues such as regulations, legal issues and cultural acceptance will be addressed.”

“AI techniques are evolving rapidly and organizations will need to invest significantly in skills, processes and tools to successfully exploit these techniques and build AI-enhanced systems,” added Mr. Cearley. “Investment areas can include data preparation, integration, algorithm and training methodology selection, and model creation. Multiple constituencies including data scientists, developers and business process owners will need to work together.”

The first three strategic technology trends explore how artificial intelligence (AI) and machine learning are seeping into virtually everything and represent a major battleground for technology providers over the next five years. The next four trends focus on blending the digital and physical worlds to create an immersive, digitally enhanced environment. The last three refer to exploiting connections between an expanding set of people and businesses, as well as devices, content and services to deliver digital business outcomes.

Cearley adds, “Intelligent things are physical things that go beyond the execution of rigid programming models to exploit AI to deliver advanced behaviors and interact more naturally with their surroundings and with people. AI is driving advances for new intelligent things (such as autonomous vehicles, robots and drones) and delivering enhanced capability to many existing things (such as Internet of Things [IoT] connected consumer and industrial systems).”

Next time: Examining widespread ADS/ADAS (advanced driver assistance systems) efforts worldwide continues.

Observations & Opportunities: AI & Autonomous Vehicles—Awesome Implications

Simply put, the business and technology implications for autonomous vehicles, or Automated Driving Systems (ADSs) per the U.S. Department of Transportation (DoT), for those making or using vacuum equipment and related materials, are enormous. These vehicles will require powerful and reliable ICs plus sophisticated MEMS sensors and actuators—all made with vacuum-centric technology to varying degrees.

Will ADSs be widely accepted and their use be on a massive scale? No one seems to know when it will happen on a large scale but some huge global companies are betting that it will be soon. The driving public may be skeptical of Autonomous Vehicles (AVs) at the moment but that was true with personal computers and smartphones too. Before the iPhone just a little over a decade ago, how many of us would have thought that today’s reliance on smartphones, laptops and other mobile devices would be so pervasive, even for TV experiences, on the smaller screens?

Many huge manufacturing and high-tech firms using contract manufacturers to build their products are involved that are not part the automobile industry itself. Insurance companies would really like ADS as would cities where gridlock traffic and accidents are a constant problem as are driver distractions. Adding some control to vehicles could help reduce accidents and traffic jams.

With President Trump proclaiming that massive infrastructure investment is needed, as have several previous presidents, it might actually happen this time. If roads are going to be rebuilt for the 21st Century, then we need smarter roads, bridges, etc. —computational devices, sensors, actuators, precise positioning information, and other technologies to make ADS vehicles safe and reliable. ADSs absolutely rely upon Artificial Intelligence (AI) and that is computational intensive. The value of electrical/electronics content in new vehicles now exceeds the mechanical component costs.

Autonomous Vehicles Defined
So what exactly is an autonomous vehicle or ADS?

“An autonomous car (also known as a driverless car, auto, self-driving car, robotic car) and Unmanned Ground Vehicle is a vehicle that is capable of sensing its environment and navigating without human input. Many such systems are evolving, but as of 2017 no cars permitted on public roads were fully autonomous. They all require a human at the wheel who must be ready to take control at any time.” per Wikipedia [https://en.wikipedia.org/wiki/Autonomous_car]. Intel Corp. uses automotive Advanced Driver Assistance Systems (ADAS) terminology.

Caption: Courtesy of Intel Corporation


With so many distracted drivers making your commute or everyday driving trips hazardous, perhaps ADSs does offer some compelling advantages worth consideration.

The U.S. DoT recently issued their “Automated Driving Systems (ADSs)” report. In it, Secretary Elaine L. Chao says, “Today, our country is on the verge of one of the most exciting and important innovations in transportation history— the development of Automated Driving Systems (ADSs), commonly referred to as automated or self-driving vehicles.

“The future of this new technology is so full of promise. It’s a future where vehicles increasingly help drivers avoid crashes. It’s a future where the time spent commuting is dramatically reduced, and where millions more—including the elderly and people with disabilities–gain access to the freedom of the open road. And, especially important, it’s a future where highway fatalities and injuries are significantly reduced.

“Since the Department of Transportation was established in 1966, there have been more than 2.2 million motor- vehicle-related fatalities in the United States. In addition, after decades of decline, motor vehicle fatalities spiked by more than 7.2 percent in 2015, the largest single-year increase since 1966. The major factor in 94 percent of all fatal crashes is human error. So ADSs have the potential to significantly reduce highway fatalities by addressing the root cause of these tragic crashes.”

A report from AAA reveals that the majority of U.S. drivers seek autonomous technologies in their next vehicle, but they continue to fear the fully self-driving car, so far. Despite the prospect that autonomous vehicles will be safer, more efficient and more convenient than their human-driven counterparts, three-quarters of U.S. drivers report feeling afraid to ride in a self-driving car, and only 10 percent report that they’d actually feel safer sharing the roads with driverless vehicles. As automakers press forward in the development of autonomous vehicles, AAA urges the gradual, safe introduction of these technologies to ensure that American drivers are informed, prepared and comfortable with this shift in mobility. [http://newsroom.aaa.com/2017/03/americans-feel-unsafe-sharing-road-fully-self-driving-cars/]

From a practical perspective, it will take a while to improve infrastructure that can accommodate ADSs and for the inevitable tweaking of the many ADS systems.

Tech Giants Pushing ADS Technology

For those exploring ADS technology’s opportunities, consider SAE International’s efforts to date. It is a global association of more than 128,000 engineers and related technical experts in the aerospace, automotive and commercial-vehicle industries [https://www.sae.org/misc/pdfs/automated_driving.pdf]. It’s worth reading their description of then levels of Driving Automation to better understand the challenges involved. Although it certainly addresses vehicles, it also has aerospace implications. Military drones today, and some missiles, use AI to perform their assigned tasks. Decades ago, fully automated aircraft with automated air traffic control (ATC) was demonstrated.

A few weeks ago, Brian Krzanich, the chief executive officer of Intel Corp., announced the fact that Waymo and Intel were collaborating on self-driving car technology.

Autonomous Driving will End Human Driving Errors and Lead to Safer Roads for Everyone.
Per Mr. Krzanich, “One of the big promises of artificial intelligence (AI) is our driverless future. Nearly 1.3 million people die in road crashes worldwide every year – an average 3,287 deaths a day1. Nearly 90 percent of those collisions are caused by human error. Self-driving technology can help prevent these errors by giving autonomous vehicles the capacity to learn from the collective experience of millions of cars – avoiding the mistakes of others and creating a safer driving environment.

“Given the pace at which autonomous driving is coming to life, I fully expect my children’s children will never have to drive a car. That’s an astounding thought: Something almost 90 percent of Americans do every day will end within a generation. With so much life-saving potential, it’s a rapid transformation that Intel is excited to be at the forefront of along with other industry leaders like Waymo.

“Waymo’s newest vehicles, the self-driving Chrysler Pacifica hybrid minivans, feature Intel-based technologies for sensor processing, general compute and connectivity, enabling real-time decisions for full autonomy in city conditions. As Waymo’s self-driving technology becomes smarter and more capable, its high-performance hardware and software will require even more powerful and efficient compute. By working closely with Waymo, Intel can offer Waymo’s fleet of vehicles the advanced processing power required for level 4 and 5 autonomy.

“With 3 million miles of real-world driving, Waymo cars with Intel technology inside have already processed more self-driving car miles than any other autonomous fleet on U.S. roads. Intel’s collaboration with Waymo ensures Intel will continue its leading role in helping realize the promise of autonomous driving and a safer, collision-free future,” added Krzanich

Consumer Adoption of ADS Driving & On-Demand Car Services
The Gartner Consumer Trends in Automotive online survey, conducted from April 2017 through May 2017, polled 1,519 people in the U.S. and Germany, found that 55 percent of respondents will not consider riding in a fully autonomous vehicle, while 71 percent may consider riding in a partially autonomous vehicle. Gartner, Inc. is a global research and advisory company [http://www.gartner.com/technology/home.jsp].

The Gartner survey say that “…concerns around technology failures and security are key reasons why many consumers are cautious about fully autonomous vehicles. Fear of autonomous vehicles getting confused by unexpected situations, safety concerns around equipment and system failures and vehicle and system security are top concerns around using fully autonomous vehicles,” explains Mike Ramsey, research director at Gartner. Survey respondents agreed that fully autonomous vehicles do offer many advantages, including improved fuel economy and a reduced number and severity of crashes. Additional benefits they identified include were having a safe transportation option when drivers are tired and using travel time for entertainment and work.

The survey also found that consumers who currently embrace on-demand car services are more likely to ride in and purchase partially and fully autonomous vehicles. “This signifies that these more evolved users of transportation methods are more open toward the concept of autonomous cars,” said Mr. Ramsey.

Next time: More on widespread ADS/ADAS (advanced driver assistance systems) efforts worldwide.