Tag Archives: Ju Li

Combining silicon with metal oxide memristors to create powerful, low-energy intensive chips enabling AI in portable devices

In this one week, I’m publishing my first stories (see also June 13, 2023 posting “ChatGPT and a neuromorphic [brainlike] synapse“) where artificial intelligence (AI) software is combined with a memristor (hardware component) for brainlike (neuromorphic) computing.

Here’s more about some of the latest research from a March 30, 2023 news item on ScienceDaily,

Everyone is talking about the newest AI and the power of neural networks, forgetting that software is limited by the hardware on which it runs. But it is hardware, says USC [University of Southern California] Professor of Electrical and Computer Engineering Joshua Yang, that has become “the bottleneck.” Now, Yang’s new research with collaborators might change that. They believe that they have developed a new type of chip with the best memory of any chip thus far for edge AI (AI in portable devices).

A March 29, 2023 University of Southern California (USC) news release (also on EurekAlert), which originated the news item, contextualizes the research and delves further into the topic of neuromorphic hardware,

For approximately the past 30 years, while the size of the neural networks needed for AI and data science applications doubled every 3.5 months, the hardware capability needed to process them doubled only every 3.5 years. According to Yang, hardware presents a more and more severe problem for which few have patience. 

Governments, industry, and academia are trying to address this hardware challenge worldwide. Some continue to work on hardware solutions with silicon chips, while others are experimenting with new types of materials and devices.  Yang’s work falls into the middle—focusing on exploiting and combining the advantages of the new materials and traditional silicon technology that could support heavy AI and data science computation. 

Their new paper in Nature focuses on the understanding of fundamental physics that leads to a drastic increase in memory capacity needed for AI hardware. The team led by Yang, with researchers from USC (including Han Wang’s group), MIT [Massachusetts Institute of Technology], and the University of Massachusetts, developed a protocol for devices to reduce “noise” and demonstrated the practicality of using this protocol in integrated chips. This demonstration was made at TetraMem, a startup company co-founded by Yang and his co-authors  (Miao Hu, Qiangfei Xia, and Glenn Ge), to commercialize AI acceleration technology. According to Yang, this new memory chip has the highest information density per device (11 bits) among all types of known memory technologies thus far. Such small but powerful devices could play a critical role in bringing incredible power to the devices in our pockets. The chips are not just for memory but also for the processor. And millions of them in a small chip, working in parallel to rapidly run your AI tasks, could only require a small battery to power it. 

The chips that Yang and his colleagues are creating combine silicon with metal oxide memristors in order to create powerful but low-energy intensive chips. The technique focuses on using the positions of atoms to represent information rather than the number of electrons (which is the current technique involved in computations on chips). The positions of the atoms offer a compact and stable way to store more information in an analog, instead of digital fashion. Moreover, the information can be processed where it is stored instead of being sent to one of the few dedicated ‘processors,’ eliminating the so-called ‘von Neumann bottleneck’ existing in current computing systems.  In this way, says Yang, computing for AI is “more energy efficient with a higher throughput.”

How it works: 

Yang explains that electrons which are manipulated in traditional chips, are “light.” And this lightness, makes them prone to moving around and being more volatile.  Instead of storing memory through electrons, Yang and collaborators are storing memory in full atoms. Here is why this memory matters. Normally, says Yang, when one turns off a computer, the information memory is gone—but if you need that memory to run a new computation and your computer needs the information all over again, you have lost both time and energy.  This new method, focusing on activating atoms rather than electrons, does not require battery power to maintain stored information. Similar scenarios happen in AI computations, where a stable memory capable of high information density is crucial. Yang imagines this new tech that may enable powerful AI capability in edge devices, such as Google Glasses, which he says previously suffered from a frequent recharging issue.

Further, by converting chips to rely on atoms as opposed to electrons, chips become smaller.  Yang adds that with this new method, there is more computing capacity at a smaller scale. And this method, he says, could offer “many more levels of memory to help increase information density.” 

To put it in context, right now, ChatGPT is running on a cloud. The new innovation, followed by some further development, could put the power of a mini version of ChatGPT in everyone’s personal device. It could make such high-powered tech more affordable and accessible for all sorts of applications. 

Here’s a link to and a citation for the paper,

Thousands of conductance levels in memristors integrated on CMOS by Mingyi Rao, Hao Tang, Jiangbin Wu, Wenhao Song, Max Zhang, Wenbo Yin, Ye Zhuo, Fatemeh Kiani, Benjamin Chen, Xiangqi Jiang, Hefei Liu, Hung-Yu Chen, Rivu Midya, Fan Ye, Hao Jiang, Zhongrui Wang, Mingche Wu, Miao Hu, Han Wang, Qiangfei Xia, Ning Ge, Ju Li & J. Joshua Yang. Nature volume 615, pages 823–829 (2023) DOI: https://doi.org/10.1038/s41586-023-05759-5 Issue Date: 30 March 2023 Published: 29 March 2023

This paper is behind a paywall.

Stretching diamonds to improve electronic devices

On the last day of 2020, City University of Hong Kong (CityU) announced a technique for stretching diamonds that could result in a new generation of electronic devices. A December 31, 2020 news item on ScienceDaily makes the announcement,

Diamond is the hardest material in nature. It also has great potential as an excellent electronic material. A research team has demonstrated for the first time the large, uniform tensile elastic straining of microfabricated diamond arrays through the nanomechanical approach. Their findings have shown the potential of strained diamonds as prime candidates for advanced functional devices in microelectronics, photonics, and quantum information technologies.

A December 31, 2020 CityU press release on EurekAlert , which originated the news item, delves further into the research,

The research was co-led by Dr Lu Yang, Associate Professor in the Department of Mechanical Engineering (MNE) at CityU and researchers from Massachusetts Institute of Technology (MIT) and Harbin Institute of Technology (HIT). Their findings have been recently published in the prestigious scientific journal Science, titled “Achieving large uniform tensile elasticity in microfabricated diamond“.

“This is the first time showing the extremely large, uniform elasticity of diamond by tensile experiments. Our findings demonstrate the possibility of developing electronic devices through ‘deep elastic strain engineering’ of microfabricated diamond structures,” said Dr Lu.

Diamond: “Mount Everest” of electronic materials

Well known for its hardness, industrial applications of diamonds are usually cutting, drilling, or grinding. But diamond is also considered as a high-performance electronic and photonic material due to its ultra-high thermal conductivity, exceptional electric charge carrier mobility, high breakdown strength and ultra-wide bandgap. Bandgap is a key property in semi-conductor, and wide bandgap allows operation of high-power or high-frequency devices. “That’s why diamond can be considered as ‘Mount Everest’ of electronic materials, possessing all these excellent properties,” Dr Lu said.

However, the large bandgap and tight crystal structure of diamond make it difficult to “dope”, a common way to modulate the semi-conductors’ electronic properties during production, hence hampering the diamond’s industrial application in electronic and optoelectronic devices. A potential alternative is by “strain engineering”, that is to apply very large lattice strain, to change the electronic band structure and associated functional properties. But it was considered as “impossible” for diamond due to its extremely high hardness.

Then in 2018, Dr Lu and his collaborators discovered that, surprisingly, nanoscale diamond can be elastically bent with unexpected large local strain. This discovery suggests the change of physical properties in diamond through elastic strain engineering can be possible. Based on this, the latest study showed how this phenomenon can be utilized for developing functional diamond devices.

Uniform tensile straining across the sample

The team firstly microfabricated single-crystalline diamond samples from a solid diamond single crystals. The samples were in bridge-like shape – about one micrometre long and 300 nanometres wide, with both ends wider for gripping (See image: Tensile straining of diamond bridges). The diamond bridges were then uniaxially stretched in a well-controlled manner within an electron microscope. Under cycles of continuous and controllable loading-unloading of quantitative tensile tests, the diamond bridges demonstrated a highly uniform, large elastic deformation of about 7.5% strain across the whole gauge section of the specimen, rather than deforming at a localized area in bending. And they recovered their original shape after unloading.

By further optimizing the sample geometry using the American Society for Testing and Materials (ASTM) standard, they achieved a maximum uniform tensile strain of up to 9.7%, which even surpassed the maximum local value in the 2018 study, and was close to the theoretical elastic limit of diamond. More importantly, to demonstrate the strained diamond device concept, the team also realized elastic straining of microfabricated diamond arrays.

Tuning the bandgap by elastic strains

The team then performed density functional theory (DFT) calculations to estimate the impact of elastic straining from 0 to 12% on the diamond’s electronic properties. The simulation results indicated that the bandgap of diamond generally decreased as the tensile strain increased, with the largest bandgap reduction rate down from about 5 eV to 3 eV at around 9% strain along a specific crystalline orientation. The team performed an electron energy-loss spectroscopy analysis on a pre-strained diamond sample and verified this bandgap decreasing trend.

Their calculation results also showed that, interestingly, the bandgap could change from indirect to direct with the tensile strains larger than 9% along another crystalline orientation. Direct bandgap in semi-conductor means an electron can directly emit a photon, allowing many optoelectronic applications with higher efficiency.

These findings are an early step in achieving deep elastic strain engineering of microfabricated diamonds. By nanomechanical approach, the team demonstrated that the diamond’s band structure can be changed, and more importantly, these changes can be continuous and reversible, allowing different applications, from micro/nanoelectromechanical systems (MEMS/NEMS), strain-engineered transistors, to novel optoelectronic and quantum technologies. “I believe a new era for diamond is ahead of us,” said Dr Lu.

Here’s an illustration provided by the researchers,

Caption: Stretching of microfabricated diamonds pave ways for applications in next-generation microelectronics.. Credit: Dang Chaoqun / City University of Hong Kong

Here’s a link to and a citation for the paper,

Achieving large uniform tensile elasticity in microfabricated diamond by Chaoqun Dang, Jyh-Pin Chou, Bing Dai, Chang-Ti Chou, Yang Yang, Rong Fan, Weitong Lin, Fanling Meng, Alice Hu, Jiaqi Zhu, Jiecai Han, Andrew M. Minor, Ju Li, Yang Lu. Science 01 Jan 2021: Vol. 371, Issue 6524, pp. 76-78 DOI: 10.1126/science.abc4174

This paper is behind a paywall.

Energy-efficient artificial synapse

This is the second neuromorphic computing chip story from MIT this summer in what has turned out to be a bumper crop of research announcements in this field. The first MIT synapse story was featured in a June 16, 2020 posting. Now, there’s a second and completely different team announcing results for their artificial brain synapse work in a June 19, 2020 news item on Nanowerk (Note: A link has been removed),

Teams around the world are building ever more sophisticated artificial intelligence systems of a type called neural networks, designed in some ways to mimic the wiring of the brain, for carrying out tasks such as computer vision and natural language processing.

Using state-of-the-art semiconductor circuits to simulate neural networks requires large amounts of memory and high power consumption. Now, an MIT [Massachusetts Institute of Technology] team has made strides toward an alternative system, which uses physical, analog devices that can much more efficiently mimic brain processes.

The findings are described in the journal Nature Communications (“Protonic solid-state electrochemical synapse for physical neural networks”), in a paper by MIT professors Bilge Yildiz, Ju Li, and Jesús del Alamo, and nine others at MIT and Brookhaven National Laboratory. The first author of the paper is Xiahui Yao, a former MIT postdoc now working on energy storage at GRU Energy Lab.

That description of the work is one pretty much every team working on developing memristive (neuromorphic) chips could use.

On other fronts, the team has produced a very attractive illustration accompanying this research (aside: Is it my imagination or has there been a serious investment in the colour pink and other pastels for science illustrations?),

A new system developed at MIT and Brookhaven National Lab could provide a faster, more reliable and much more energy efficient approach to physical neural networks, by using analog ionic-electronic devices to mimic synapses.. Courtesy of the researchers

A June 19, 2020 MIT news release, which originated the news item, provides more insight into this specific piece of research (hint: it’s about energy use and repeatability),

Neural networks attempt to simulate the way learning takes place in the brain, which is based on the gradual strengthening or weakening of the connections between neurons, known as synapses. The core component of this physical neural network is the resistive switch, whose electronic conductance can be controlled electrically. This control, or modulation, emulates the strengthening and weakening of synapses in the brain.

In neural networks using conventional silicon microchip technology, the simulation of these synapses is a very energy-intensive process. To improve efficiency and enable more ambitious neural network goals, researchers in recent years have been exploring a number of physical devices that could more directly mimic the way synapses gradually strengthen and weaken during learning and forgetting.

Most candidate analog resistive devices so far for such simulated synapses have either been very inefficient, in terms of energy use, or performed inconsistently from one device to another or one cycle to the next. The new system, the researchers say, overcomes both of these challenges. “We’re addressing not only the energy challenge, but also the repeatability-related challenge that is pervasive in some of the existing concepts out there,” says Yildiz, who is a professor of nuclear science and engineering and of materials science and engineering.

“I think the bottleneck today for building [neural network] applications is energy efficiency. It just takes too much energy to train these systems, particularly for applications on the edge, like autonomous cars,” says del Alamo, who is the Donner Professor in the Department of Electrical Engineering and Computer Science. Many such demanding applications are simply not feasible with today’s technology, he adds.

The resistive switch in this work is an electrochemical device, which is made of tungsten trioxide (WO3) and works in a way similar to the charging and discharging of batteries. Ions, in this case protons, can migrate into or out of the crystalline lattice of the material,  explains Yildiz, depending on the polarity and strength of an applied voltage. These changes remain in place until altered by a reverse applied voltage — just as the strengthening or weakening of synapses does.

The mechanism is similar to the doping of semiconductors,” says Li, who is also a professor of nuclear science and engineering and of materials science and engineering. In that process, the conductivity of silicon can be changed by many orders of magnitude by introducing foreign ions into the silicon lattice. “Traditionally those ions were implanted at the factory,” he says, but with the new device, the ions are pumped in and out of the lattice in a dynamic, ongoing process. The researchers can control how much of the “dopant” ions go in or out by controlling the voltage, and “we’ve demonstrated a very good repeatability and energy efficiency,” he says.

Yildiz adds that this process is “very similar to how the synapses of the biological brain work. There, we’re not working with protons, but with other ions such as calcium, potassium, magnesium, etc., and by moving those ions you actually change the resistance of the synapses, and that is an element of learning.” The process taking place in the tungsten trioxide in their device is similar to the resistance modulation taking place in biological synapses, she says.

“What we have demonstrated here,” Yildiz says, “even though it’s not an optimized device, gets to the order of energy consumption per unit area per unit change in conductance that’s close to that in the brain.” Trying to accomplish the same task with conventional CMOS type semiconductors would take a million times more energy, she says.

The materials used in the demonstration of the new device were chosen for their compatibility with present semiconductor manufacturing systems, according to Li. But they include a polymer material that limits the device’s tolerance for heat, so the team is still searching for other variations of the device’s proton-conducting membrane and better ways of encapsulating its hydrogen source for long-term operations.

“There’s a lot of fundamental research to be done at the materials level for this device,” Yildiz says. Ongoing research will include “work on how to integrate these devices with existing CMOS transistors” adds del Alamo. “All that takes time,” he says, “and it presents tremendous opportunities for innovation, great opportunities for our students to launch their careers.”

Coincidentally or not a University of Massachusetts at Amherst team announced memristor voltage use comparable to human brain voltage use (see my June 15, 2020 posting), plus, there’s a team at Stanford University touting their low-energy biohybrid synapse in a XXX posting. (June 2020 has been a particularly busy month here for ‘artificial brain’ or ‘memristor’ stories.)

Getting back to this latest MIT research, here’s a link to and a citation for the paper,

Protonic solid-state electrochemical synapse for physical neural networks by Xiahui Yao, Konstantin Klyukin, Wenjie Lu, Murat Onen, Seungchan Ryu, Dongha Kim, Nicolas Emond, Iradwikanari Waluyo, Adrian Hunt, Jesús A. del Alamo, Ju Li & Bilge Yildiz. Nature Communications volume 11, Article number: 3134 (2020) DOI: https://doi.org/10.1038/s41467-020-16866-6 Published: 19 June 2020

This paper is open access.

Mixing the unmixable for all new nanoparticles

This news comes out of the University of Maryland and the discovery could led to nanoparticles that have never before been imagined. From a March 29, 2018 news item on ScienceDaily,

Making a giant leap in the ‘tiny’ field of nanoscience, a multi-institutional team of researchers is the first to create nanoscale particles composed of up to eight distinct elements generally known to be immiscible, or incapable of being mixed or blended together. The blending of multiple, unmixable elements into a unified, homogenous nanostructure, called a high entropy alloy nanoparticle, greatly expands the landscape of nanomaterials — and what we can do with them.

This research makes a significant advance on previous efforts that have typically produced nanoparticles limited to only three different elements and to structures that do not mix evenly. Essentially, it is extremely difficult to squeeze and blend different elements into individual particles at the nanoscale. The team, which includes lead researchers at University of Maryland, College Park (UMD)’s A. James Clark School of Engineering, published a peer-reviewed paper based on the research featured on the March 30 [2018] cover of Science.

A March 29, 2018 University of Maryland press release (also on EurekAlert), which originated the news item, delves further (Note: Links have been removed),

“Imagine the elements that combine to make nanoparticles as Lego building blocks. If you have only one to three colors and sizes, then you are limited by what combinations you can use and what structures you can assemble,” explains Liangbing Hu, associate professor of materials science and engineering at UMD and one of the corresponding authors of the paper. “What our team has done is essentially enlarged the toy chest in nanoparticle synthesis; now, we are able to build nanomaterials with nearly all metallic and semiconductor elements.”

The researchers say this advance in nanoscience opens vast opportunities for a wide range of applications that includes catalysis (the acceleration of a chemical reaction by a catalyst), energy storage (batteries or supercapacitors), and bio/plasmonic imaging, among others.

To create the high entropy alloy nanoparticles, the researchers employed a two-step method of flash heating followed by flash cooling. Metallic elements such as platinum, nickel, iron, cobalt, gold, copper, and others were exposed to a rapid thermal shock of approximately 3,000 degrees Fahrenheit, or about half the temperature of the sun, for 0.055 seconds. The extremely high temperature resulted in uniform mixtures of the multiple elements. The subsequent rapid cooling (more than 100,000 degrees Fahrenheit per second) stabilized the newly mixed elements into the uniform nanomaterial.

“Our method is simple, but one that nobody else has applied to the creation of nanoparticles. By using a physical science approach, rather than a traditional chemistry approach, we have achieved something unprecedented,” says Yonggang Yao, a Ph.D. student at UMD and one of the lead authors of the paper.

To demonstrate one potential use of the nanoparticles, the research team used them as advanced catalysts for ammonia oxidation, which is a key step in the production of nitric acid (a liquid acid that is used in the production of ammonium nitrate for fertilizers, making plastics, and in the manufacturing of dyes). They were able to achieve 100 percent oxidation of ammonia and 99 percent selectivity toward desired products with the high entropy alloy nanoparticles, proving their ability as highly efficient catalysts.

Yao says another potential use of the nanoparticles as catalysts could be the generation of chemicals or fuels from carbon dioxide.

“The potential applications for high entropy alloy nanoparticles are not limited to the field of catalysis. With cross-discipline curiosity, the demonstrated applications of these particles will become even more widespread,” says Steven D. Lacey, a Ph.D. student at UMD and also one of the lead authors of the paper.

This research was performed through a multi-institutional collaboration of Prof. Liangbing Hu’s group at the University of Maryland, College Park; Prof. Reza Shahbazian-Yassar’s group at University of Illinois at Chicago; Prof. Ju Li’s group at the Massachusetts Institute of Technology; Prof. Chao Wang’s group at Johns Hopkins University; and Prof. Michael Zachariah’s group at the University of Maryland, College Park.

What outside experts are saying about this research:

“This is quite amazing; Dr. Hu creatively came up with this powerful technique, carbo-thermal shock synthesis, to produce high entropy alloys of up to eight different elements in a single nanoparticle. This is indeed unthinkable for bulk materials synthesis. This is yet another beautiful example of nanoscience!,” says Peidong Yang, the S.K. and Angela Chan Distinguished Professor of Energy and professor of chemistry at the University of California, Berkeley and member of the American Academy of Arts and Sciences.

“This discovery opens many new directions. There are simulation opportunities to understand the electronic structure of the various compositions and phases that are important for the next generation of catalyst design. Also, finding correlations among synthesis routes, composition, and phase structure and performance enables a paradigm shift toward guided synthesis,” says George Crabtree, Argonne Distinguished Fellow and director of the Joint Center for Energy Storage Research at Argonne National Laboratory.

More from the research coauthors:

“Understanding the atomic order and crystalline structure in these multi-element nanoparticles reveals how the synthesis can be tuned to optimize their performance. It would be quite interesting to further explore the underlying atomistic mechanisms of the nucleation and growth of high entropy alloy nanoparticle,” says Reza Shahbazian-Yassar, associate professor at the University of Illinois at Chicago and a corresponding author of the paper.

“Carbon metabolism drives ‘living’ metal catalysts that frequently move around, split, or merge, resulting in a nanoparticle size distribution that’s far from the ordinary, and highly tunable,” says Ju Li, professor at the Massachusetts Institute of Technology and a corresponding author of the paper.

“This method enables new combinations of metals that do not exist in nature and do not otherwise go together. It enables robust tuning of the composition of catalytic materials to optimize the activity, selectivity, and stability, and the application will be very broad in energy conversions and chemical transformations,” says Chao Wang, assistant professor of chemical and biomolecular engineering at Johns Hopkins University and one of the study’s authors.

Here’s a link to and a citation for the paper,

Carbothermal shock synthesis of high-entropy-alloy nanoparticles by Yonggang Yao, Zhennan Huang, Pengfei Xie, Steven D. Lacey, Rohit Jiji Jacob, Hua Xie, Fengjuan Chen, Anmin Nie, Tiancheng Pu, Miles Rehwoldt, Daiwei Yu, Michael R. Zachariah, Chao Wang, Reza Shahbazian-Yassar, Ju Li, Liangbing Hu. Science 30 Mar 2018: Vol. 359, Issue 6383, pp. 1489-1494 DOI: 10.1126/science.aan5412

This paper is behind a paywall.

IBM and its working 7nm test chip

I wrote abut IBM and its plans for a 7nm computer chip last year in a July 11, 2014 posting, which featured IBM and mention of HP Labs and other company’s plans for shrinking their computer chips. Almost one year later, IBM has announced, in a July 9, 2015 IBM news release on PRnewswire.com the accomplishment of a working 7nm test chip,

An alliance led by IBM Research (NYSE: IBM) today announced that it has produced the semiconductor industry’s first 7nm (nanometer) node test chips with functioning transistors.  The breakthrough, accomplished in partnership with GLOBALFOUNDRIES and Samsung at SUNY Polytechnic Institute’s Colleges of Nanoscale Science and Engineering (SUNY Poly CNSE), could result in the ability to place more than 20 billion tiny switches — transistors — on the fingernail-sized chips that power everything from smartphones to spacecraft.

To achieve the higher performance, lower power and scaling benefits promised by 7nm technology, researchers had to bypass conventional semiconductor manufacturing approaches. Among the novel processes and techniques pioneered by the IBM Research alliance were a number of industry-first innovations, most notably Silicon Germanium (SiGe) channel transistors and Extreme Ultraviolet (EUV) lithography integration at multiple levels.

Industry experts consider 7nm technology crucial to meeting the anticipated demands of future cloud computing and Big Data systems, cognitive computing, mobile products and other emerging technologies. Part of IBM’s $3 billion, five-year investment in chip R&D (announced in 2014), this accomplishment was made possible through a unique public-private partnership with New York State and joint development alliance with GLOBALFOUNDRIES, Samsung and equipment suppliers. The team is based at SUNY Poly’s NanoTech Complex in Albany [New York state].

“For business and society to get the most out of tomorrow’s computers and devices, scaling to 7nm and beyond is essential,” said Arvind Krishna, senior vice president and director of IBM Research. “That’s why IBM has remained committed to an aggressive basic research agenda that continually pushes the limits of semiconductor technology. Working with our partners, this milestone builds on decades of research that has set the pace for the microelectronics industry, and positions us to advance our leadership for years to come.”

Microprocessors utilizing 22nm and 14nm technology power today’s servers, cloud data centers and mobile devices, and 10nm technology is well on the way to becoming a mature technology. The IBM Research-led alliance achieved close to 50 percent area scaling improvements over today’s most advanced technology, introduced SiGe channel material for transistor performance enhancement at 7nm node geometries, process innovations to stack them below 30nm pitch and full integration of EUV lithography at multiple levels. These techniques and scaling could result in at least a 50 percent power/performance improvement for next generation mainframe and POWER systems that will power the Big Data, cloud and mobile era.

“Governor Andrew Cuomo’s trailblazing public-private partnership model is catalyzing historic innovation and advancement. Today’s [July 8, 2015] announcement is just one example of our collaboration with IBM, which furthers New York State’s global leadership in developing next generation technologies,” said Dr. Michael Liehr, SUNY Poly Executive Vice President of Innovation and Technology and Vice President of Research.  “Enabling the first 7nm node transistors is a significant milestone for the entire semiconductor industry as we continue to push beyond the limitations of our current capabilities.”

“Today’s announcement marks the latest achievement in our long history of collaboration to accelerate development of next-generation technology,” said Gary Patton, CTO and Head of Worldwide R&D at GLOBALFOUNDRIES. “Through this joint collaborative program based at the Albany NanoTech Complex, we are able to maintain our focus on technology leadership for our clients and partners by helping to address the development challenges central to producing a smaller, faster, more cost efficient generation of semiconductors.”

The 7nm node milestone continues IBM’s legacy of historic contributions to silicon and semiconductor innovation. They include the invention or first implementation of the single cell DRAM, the Dennard Scaling Laws, chemically amplified photoresists, copper interconnect wiring, Silicon on Insulator, strained engineering, multi core microprocessors, immersion lithography, high speed SiGe, High-k gate dielectrics, embedded DRAM, 3D chip stacking and Air gap insulators.

In 2014, they were talking about carbon nanotubes with regard to the 7nm chip, this shift to silicon germanium is interesting.

Sebastian Anthony in a July 9, 2015 article for Ars Technica offers some intriguing insight into the accomplishment and the technology (Note: A link has been removed),

… While it should be stressed that commercial 7nm chips remain at least two years away, this test chip from IBM and its partners is extremely significant for three reasons: it’s a working sub-10nm chip (this is pretty significant in itself); it’s the first commercially viable sub-10nm FinFET logic chip that uses silicon-germanium as the channel material; and it appears to be the first commercially viable design produced with extreme ultraviolet (EUV) lithography.

Technologically, SiGe and EUV are both very significant. SiGe has higher electron mobility than pure silicon, which makes it better suited for smaller transistors. The gap between two silicon nuclei is about 0.5nm; as the gate width gets ever smaller (about 7nm in this case), the channel becomes so small that the handful of silicon atoms can’t carry enough current. By mixing some germanium into the channel, electron mobility increases, and adequate current can flow. Silicon generally runs into problems at sub-10nm nodes, and we can expect Intel and TSMC to follow a similar path to IBM, GlobalFoundries, and Samsung (aka the Common Platform alliance).

EUV lithography is an more interesting innovation. Basically, as chip features get smaller, you need a narrower beam of light to etch those features accurately, or you need to use multiple patterning (which we won’t go into here). The current state of the art for lithography is a 193nm ArF (argon fluoride) laser; that is, the wavelength is 193nm wide. Complex optics and multiple painstaking steps are required to etch 14nm features using a 193nm light source. EUV has a wavelength of just 13.5nm, which will handily take us down into the sub-10nm realm, but so far it has proven very difficult and expensive to deploy commercially (it has been just around the corner for quite a few years now).

If you’re interested in the nuances, I recommend reading Anthony’s article in its entirety.

One final comment, there was no discussion of electrodes or other metallic components associated with computer chips. The metallic components are a topic of some interest to me (anyway), given some research published by scientists at the Massachusetts Institute of Technology (MIT) last year. From my Oct. 14, 2014 posting,

Research from the Massachusetts Institute of Technology (MIT) has revealed a new property of metal nanoparticles, in this case, silver. From an Oct. 12, 2014 news item on ScienceDaily,

A surprising phenomenon has been found in metal nanoparticles: They appear, from the outside, to be liquid droplets, wobbling and readily changing shape, while their interiors retain a perfectly stable crystal configuration.

The research team behind the finding, led by MIT professor Ju Li, says the work could have important implications for the design of components in nanotechnology, such as metal contacts for molecular electronic circuits. [my emphasis added]

This discovery and others regarding materials and phase changes at ever diminishing sizes hint that a computer with a functioning 7nm chip might be a bit further off than IBM is suggesting.

Silver nanoparticles: liquid on the outside, crystal on the inside

Research from the Massachusetts Institute of Technology (MIT) has revealed a new property of metal nanoparticles, in this case, silver. From an Oct. 12, 2014 news item on ScienceDaily,

A surprising phenomenon has been found in metal nanoparticles: They appear, from the outside, to be liquid droplets, wobbling and readily changing shape, while their interiors retain a perfectly stable crystal configuration.

The research team behind the finding, led by MIT professor Ju Li, says the work could have important implications for the design of components in nanotechnology, such as metal contacts for molecular electronic circuits.

The results, published in the journal Nature Materials, come from a combination of laboratory analysis and computer modeling, by an international team that included researchers in China, Japan, and Pittsburgh, as well as at MIT.

An Oct. 12, 2014 MIT news release (also on EurekAlert), which originated the news item, offers both more information about the research and a surprising comparison of nanometers to the width of a human hair,

The experiments were conducted at room temperature, with particles of pure silver less than 10 nanometers across — less than one-thousandth of the width of a human hair. [emphasis mine] But the results should apply to many different metals, says Li, senior author of the paper and the BEA Professor of Nuclear Science and Engineering.

Silver has a relatively high melting point — 962 degrees Celsius, or 1763 degrees Fahrenheit — so observation of any liquidlike behavior in its nanoparticles was “quite unexpected,” Li says. Hints of the new phenomenon had been seen in earlier work with tin, which has a much lower melting point, he says.

The use of nanoparticles in applications ranging from electronics to pharmaceuticals is a lively area of research; generally, Li says, these researchers “want to form shapes, and they want these shapes to be stable, in many cases over a period of years.” So the discovery of these deformations reveals a potentially serious barrier to many such applications: For example, if gold or silver nanoligaments are used in electronic circuits, these deformations could quickly cause electrical connections to fail.

It was a bit surprising to see the reference to 10 nanometers as being less than 1/1,000th (one/one thousandth) of the width of a human hair in a news release from MIT. Generally, a nanometer has been described as being anywhere from less than 1/50,000th to 1/120,000th of the width of a human hair with less than 1/100,000th being one of the most common descriptions. While it’s true that 10 nanometers is less than 1/1,000th of the width of a human hair, it seems a bit misleading when it could be described, in keeping with the more common description, as less than 1/10,000th.

Getting back to the research, the news release offers more details as to how it was conducted,

The researchers’ detailed imaging with a transmission electron microscope and atomistic modeling revealed that while the exterior of the metal nanoparticles appears to move like a liquid, only the outermost layers — one or two atoms thick — actually move at any given time. As these outer layers of atoms move across the surface and redeposit elsewhere, they give the impression of much greater movement — but inside each particle, the atoms stay perfectly lined up, like bricks in a wall.

“The interior is crystalline, so the only mobile atoms are the first one or two monolayers,” Li says. “Everywhere except the first two layers is crystalline.”

By contrast, if the droplets were to melt to a liquid state, the orderliness of the crystal structure would be eliminated entirely — like a wall tumbling into a heap of bricks.

Technically, the particles’ deformation is pseudoelastic, meaning that the material returns to its original shape after the stresses are removed — like a squeezed rubber ball — as opposed to plasticity, as in a deformable lump of clay that retains a new shape.

The phenomenon of plasticity by interfacial diffusion was first proposed by Robert L. Coble, a professor of ceramic engineering at MIT, and is known as “Coble creep.” “What we saw is aptly called Coble pseudoelasticity,” Li says.

Now that the phenomenon has been understood, researchers working on nanocircuits or other nanodevices can quite easily compensate for it, Li says. If the nanoparticles are protected by even a vanishingly thin layer of oxide, the liquidlike behavior is almost completely eliminated, making stable circuits possible.

There are some benefits to this insight (from the news release),

On the other hand, for some applications this phenomenon might be useful: For example, in circuits where electrical contacts need to withstand rotational reconfiguration, particles designed to maximize this effect might prove useful, using noble metals or a reducing atmosphere, where the formation of an oxide layer is destabilized, Li says.

The new finding flies in the face of expectations — in part, because of a well-understood relationship, in most materials, in which mechanical strength increases as size is reduced.

“In general, the smaller the size, the higher the strength,” Li says, but “at very small sizes, a material component can get very much weaker. The transition from ‘smaller is stronger’ to ‘smaller is much weaker’ can be very sharp.”

That crossover, he says, takes place at about 10 nanometers at room temperature — a size that microchip manufacturers are approaching as circuits shrink. When this threshold is reached, Li says, it causes “a very precipitous drop” in a nanocomponent’s strength.

The findings could also help explain a number of anomalous results seen in other research on small particles, Li says.

For more details about the various attempts to create smaller computer chips, you can read my July 11, 2014 posting about IBM and its proposed 7 nanometer chip where you will also find links to announcements and posts about Intel’s smaller chips and HP Labs’ attempt to recreate computers.

As for the research into liquid-like metallic (silver) nanoparticles, here’s a link to and a citation for the paper,

Liquid-like pseudoelasticity of sub-10-nm crystalline ​silver particle by Jun Sun, Longbing He, Yu-Chieh Lo, Tao Xu, Hengchang Bi, Litao Sun, Ze Zhang, Scott X. Mao, & Ju Li. Nature Materials (2014) doi:10.1038/nmat4105 Published online 12 October 2014

This paper is behind a paywall. There is a free preview via ReadCube Access.

The relationship between Valyrian steel (from Game of Thrones), Damascus steel, and nuclear nanotechnology

There’s a very interesting June 20, 2014 posting by Charles Day on his Dayside blog (located on the Physics Today website). Day manages to relate the Game of Thrones tv series to nuclear power and nanotechnology,

The military technology of A Song of Ice and Fire, George R. R. Martin’s series of fantasy novels, is medieval with an admixture of the supernatural. Dragons aside, among the most prized weapons are swords made from Valyrian steel, which are lighter, stronger, and sharper than ordinary steel swords.

Like many of the features in the rich world of the novels and their TV adaptation, Game of Thrones, Valyrian steel has a historical inspiration. Sometime before 300 BC, metalworkers in Southern India discovered a way to make small cakes of high-carbon steel known as wootz. Thanks to black wavy bands of Fe3C particles that pervade the metal, wootz steel was already strong. …

Perhaps because the properties of wootz and Damascus steels depended, in part, on a particular kind of iron ore, the ability of metallurgists to make the alloys was lost sometime in the 18th century. In A Song of Ice and Fire, the plot plays out during an era in which making Valyrian steel is a long-lost art.

Martin’s knowledge of metallurgy is perhaps shaky. …

Interestingly, the comments on the blog posting largely concern themselves with whether George RR Martin knows anything about metallurgy. The consensus being that he does and that the problems in the Game of Thrones version of metallurgy lie with the series writers.

I first came across the Damascus steel, wootz, and carbon nanotube story in 2008 and provided a concise description on my Nanotech Mysteries wiki Middle Ages page,

Damascus steel blades were first made in the 8th century CE when they acquired a legendary status as unlike other blades they were able to cut through bone and stone while remaining sharp enough to cut a piece of silk. They were also flexible which meant they didn’t break off easily in a sword fight. The secret for making the blades died (history does not record how) about 1700 CE and there hasn’t been a new blade since.

 The blades were generally made from metal ingots prepared in India using special recipes which probably put just the right amount of carbon and other impurities into the iron. By following these recipes and following specific forging techniques craftsmen ended up making nanotubes … When these blades were nearly finished, blacksmiths would etch them with acid. This brought out the wavy light and dark lines that make Damascus swords easy to recognize.3

 It turns out part of the secret to the blade is nanotechnology. Scientists discovered this by looking at a Damascus steel blade from 1700 under an electron microscope. It seems those unknown smiths were somehow encasing cementite nanowires in carbon nanotubes then forging them into the steel blades giving them their legendary strength and flexibility.

The reference information I used then seems to be no longer available online but there is this more than acceptable alternative, a Sept. 27, 2008 postiing by Ed Yong from his Not Exactly Rocket Science blog (on ScienceBlogs.com; Note: A link has been removed),

In medieval times, crusading Christian knights cut a swathe through the Middle East in an attempt to reclaim Jerusalem from the Muslims. The Muslims in turn cut through the invaders using a very special type of sword, which quickly gained a mythical reputation among the Europeans. These ‘Damascus blades‘ were extraordinarily strong, but still flexible enough to bend from hilt to tip. And they were reputedly so sharp that they could cleave a silk scarf floating to the ground, just as readily as a knight’s body.

They were superlative weapons that gave the Muslims a great advantage, and their blacksmiths carefully guarded the secret to their manufacture. The secret eventually died out in the eighteenth century and no European smith was able to fully reproduce their method.

Two years ago, Marianne Reibold and colleagues from the University of Dresden uncovered the extraordinary secret of Damascus steel – carbon nanotubes. The smiths of old were inadvertently using nanotechnology.

Getting back to Day, he goes on to explain the Damascus/Valyrian steel connection to nuclear power (Note: Links have been removed),

Valyrian and Damascus steels were on my mind earlier this week when I attended a session at TechConnect World on the use of nanotechnology in the nuclear power industry.

Scott Anderson of Lockheed Martin gave the introductory talk. Before the Fukushima disaster, Anderson pointed out, the principal materials science challenge in the nuclear industry lay in extending the lifetime of fuel rods. Now the focus has shifted to accident-tolerant fuels and safer, more durable equipment.

Among the other speakers was MIT’s Ju Li, who described his group’s experiments with incorporating carbon nanotubes (CNTs) in aluminum to boost the metal’s resistance to radiation damage. In a reactor core, neutrons and other ionizing particles penetrate vessels, walls, and other structures, where they knock atoms off lattice sites. The cumulative effect of those displacements is to create voids and other defects that weaken the structures.

Li isn’t sure yet how the CNTs resist irradiation and toughen the aluminum, but at the end of his talk he recalled their appearance in another metal, steel.

In 2006 Peter Paufler of Dresden University of Technology and his collaborators used high-resolution transmission electron microscopy (TEM) to examine the physical and chemical microstructure of a sample of Damascus steel from the 17th century.

The saber from which the sample was taken was forged in Isfahan, Persia, by the famed blacksmith Assad Ullah. As part of their experiment, Paufler and his colleagues washed the sample in hydrochloric acid to remove Fe3C particles. A second look with TEM revealed the presence of CNTs.

There’s still active interest in researching Damascus steel blades as not all the secrets behind the blade’s extraordinary qualities have been revealed yet. There is a March 13, 2014 posting here which describes a research project where Chinese researchers are attempting (using computational software) to uncover the reason for the blade’s unique patterns,

It seems that while researchers were able to answer some questions about the blade’s qualities, researchers in China believe they may have answered the question about the blade’s unique patterns, from a March 12, 2014 news release on EurekAlert,

Blacksmiths and metallurgists in the West have been puzzled for centuries as to how the unique patterns on the famous Damascus steel blades were formed. Different mechanisms for the formation of the patterns and many methods for making the swords have been suggested and attempted, but none has produced blades with patterns matching those of the Damascus swords in the museums. The debate over the mechanism of formation of the Damascus patterns is still ongoing today. Using modern metallurgical computational software (Thermo-Calc, Stockholm, Sweden), Professor Haiwen Luo of the Central Iron and Steel Research Institute in Beijing, together with his collaborator, have analyzed the relevant published data relevant to the Damascus blades, and present a new explanation that is different from other proposed mechanisms.

At the time the researchers were hoping to have someone donate a piece of genuine Damascus steel blade. From my March 13, 2014 posting,

Note from the authors: It would be much appreciated if anyone would like to donate a piece of genuine Damascus blade for our research.

Corresponding Author:

LUO Haiwen
Email: haiwenluo@126.com

Perhaps researchers will manage to solve the puzzle of how medieval craftsman were once able to create extraordinary steel blades.