Beyond CMOS or More Moore technologies to power Next Generation Computers, communications, industrial and Defense Systems

Rajesh Uppal August 5, 2020 AI & IT, Industry Comments Off on Beyond CMOS or More Moore technologies to power Next Generation Computers, communications, industrial and Defense Systems 1,828 Views

In 1965 R&D Director at Fairchild (and later Intel co-founder) Gordon Moore predicted continued systemic declines in cost and increase in performance of integrated circuits in his paper “Cramming more components onto integrated circuits.”Moore’s Law which stated that the number of transistors on a chip will double approximately every two years has been the driver of semiconductor industry in boosting the complexity, computational performance and energy efficiency while reducing cost. It has led to substantial improvements in economic productivity and overall quality of life through proliferation of computers, communication, and other industrial and consumer electronics. Microelectronics and solid state components have also been the backbone of the military systems and were main contributors in advancement of radar, communication and electronic warfare systems.

However, Moore’s Law is becoming more and more difficult. Transistors smaller than 7 nm will experience quantum tunnelling through their logic gates. Due to the costs involved in development, 5 nm is predicted to take longer to reach market than the 2 years estimated by Moore’s law. Beyond 7 nm, major technological advances would have to be made; possible candidates include vortex laser, MOSFET-BJT dual-mode transistor, 3D packaging, microfluidic cooling, PCMOS, vacuum transistors,t-rays, extreme ultraviolet lithography, carbon nanotube transistors,silicon photonics, graphene, phosphorene, organic semiconductors, gallium arsenide, indium gallium arsenide, nano-patterning,and reconfigurable chaos-based microchips.

“You know one of the remarkable things about Moore’s Law is that Moore’s Law’s past seems preordained and ordinary, and Moore’s Law’s future is difficult and requires inventions,” Mistry told IEEE Spectrum.

Moore Law becoming more and more difficult

In April 2018, Intel announced that it had delayed high-volume 10nm production to an unspecified time in 2019. Meanwhile, its competitors, like TSMC, are beginning high volume manufacturing of 7nm alternatives. The 10nm manufacturing can now pack more than 100 million (100.8 million, to be exact) transistors in each square millimeter of chip “for the first time in our industry’s history,” said Kaizad Mistry, a vice president and co-director of logic technology at the company. Delivering more transistors in the same area means the circuitry can be made smaller, saving on cost, or it means that more functionality can be added to a chip without having to make it bigger. The 10 nm generation specifications are 34 nm from one fin to the next in the company’s FinFET transistors and 36 nm from one wire to the next in the most dense interconnect layers (down from 42 nm and 52 nm, respectively, in the previous, 14-nm chip generation).

On balance, Intel said, the company is still on a pace that roughly corresponds to a doubling of transistor density every couple of years. And by that metric, Bohr says, Intel has more than doubled its transistor density in recent years. From 22nm to 14nm, the transistor density jumped by a factor of 2.5x. And in the move from 14-nm to 10-nm chip manufacturing technology, the jump was 2.7x, from 37.5 million transistors per square millimeter to more than 100 million.

Intel calls the suite of strategies it uses to accomplish this more-than-doubling “hyperscaling.” It includes design improvements, but a big piece is the company’s approach to laying down the patterns that ultimately become the chip’s transistors and wiring. With 10-nm chips, Intel as adopted self-aligned quadruple patterning (SAQP), a similar approach that requires four passes through a lithography machine. Mistry says SAQP has one more generation in it, which would take Intel down to the feature sizes needed to produce the next generation: 7 nm. Somewhere in there, we may just see extreme ultraviolet (EUV) lithography enter the picture. EUV uses 13.5-nm radiation (pretty much X-rays) instead of 193-nm ultraviolet light for feature patterning.

Moore’s prediction is already slowing down and experts predict that will come to an end in the beginning of the 2020s. The minimum possible line width is expected to be 5 nm, which corresponds to about 20 silicon or copper atoms. Based on the current calculations, today’s finFET could run out of gas at 5nm, prompting the need for a new technology.

But the other limitation to packing more transistors onto to a chip is a physical limitation called Dennard scaling– as transistors get smaller, their power density stays constant, so that the power use stays in proportion with area. This basic law of physics has created a “Power Wall” — a barrier to clock speed — that has limited microprocessor frequency to around 4 GHz since 2005

The switching energy is approaching the thermal noise spectral density. In addition to noise, leakage currents and interconnects with high capacitances will form a problem. As dimensions approach nanometer ranges, CMOS transistors are difficult to operate because of rising power dissipation of chips and the fall in power gain of smaller transistors, soaring fabrication plant costs and finally, quantum effects in silicon will bring about an end to the ongoing miniaturization of CMOS transistors.

Handel Jones, the CEO of International Business Strategies, reckons that a fab for state-of-the-art microprocessors now costs around $7 billion. He thinks that by the time the industry produces 5nm chips (which at past rates of progress might be in the early 2020s), this could rise to over $16 billion, or nearly a third of Intel’s current annual revenue.

Two types of FET’s, the Si-nanowire FET and the alternative channel (such as GaAs and Ge) FET, have shown promise to replace current planer bulk CMOS. The Si-nanowire FET has higher on-current conduction due to their quantum nature and would have edge for adoption and more promising due to its compatibility with current Si CMOS process technologies. Tunneling FET (TFET) employs quantum mechanical band-to-band tunneling mechanism having low power and FET structure that is compatible with CMOS technology. Resonant Tunneling Diodes (RTD) can provide high speed bi-stable logic operation and tunneling based SRAMs. Single Electron Transistor (SET) provides high speed, high device density, and high power efficiency and is also compatible with CMOS.

In 2015, Intel described a lateral nanowire (or gate-all-around) FET concept for the 5-nm node. In 2016, researchers at Berkeley Lab created a transistor with a working 1-nanometer gate. The field-effect transistor utilized MoS2 as the channel material, while a carbon nanotube was used to invert the channel. The effective channel length is approximately 1 nm. However, the drain to source pitch was much bigger, with micrometer size.

Current flagship SoCs are as tiny as 7 nanometers or 7 billionths of a meter.

The chips and transistors, which form the brains of our smartphones and devices, have been shrinking each year and are fast approaching a point where it will be very hard to shrink them further. Flagship mobile chipsets have been using 10nm FinFet manufacturing for a couple of years now. 7nm is the next process shrink-down, offering improvements to silicon area and power efficiency as a result of the smaller transistor feature sizes. The trade-off is the technology needed to make 7nm chips is becoming increasingly expensive, and so are chip design costs. In fact, there will be even smaller 5nm chips next year. But that’s when things will start to get difficult.

This also marks the start of the switch over to Extreme Ultraviolet Lithography (EUV) manufacturing for chips, which requires a major investment in new technologies and is necessary for making it to 5nm and below. GlobalFoundries recently announced it is abandoning its 7nm pursuits due to these costs, in favor of refining its popular 12nm and 14nm technologies. Samsung will be moving straight to EUV with its 7nm technology this year. Meanwhile, TSMC is building its first 7nm chips with existing 193nm wavelength technology, before introducing its 7FF+ EUV process in early 2019.

With just two foundries ready to produce 7nm components in time for early 2019 products, fabless chip designers like AMD, Apple, HiSilicon, and Qualcomm are busy securing deals for their next-generation products.

Samsung paves the way for 3nm chips with breakthrough GAA technology

However in May 2019 , Samsung has announced a breakthrough in chip manufacturing that will help foundries pursue miniaturization to 3nm. And the Korean giant has a head start as it is two to three years ahead of Intel and around 12 months of TSMC, said Handel Jones, chief executive of consulting firm International Business Strategies.

The breakthrough is called gate all around, or GAA in short, which will help Samsung shrink chips to 3nm and beyond. With GAA, Samsung promises an improvement of speed performance by 35 percent, while cutting power use to 50 percent. With regards to how Samsung has achieved this, it’s being reported that Samsung has redesigned the gates that control current flow to and from the channels and used a more complex 3d architecture which is much more efficient yet more expensive. That’s one of the sacrifices that the industry will have to make for that innovation. Being more complex to build, the GAA 3nm chips will likely cost much more than the previous generation and hence could lead to fewer customers. However, Ryan Lee, the foundry business vice president of marketing, said that the cost will come down over time.

Samsung’s CPUs based on the 3nm GAA process are expected to arrive in 2021 and we also expect further refinement of high-performance GPUs to arrive in 2022.

More Moore technologies

The vision of “More Moore” Technologies is to continue to follow the exponential reduction in size of electronic devices by migrating from charge to non-charge based devices i.e. based on spin, molecular state, photons, phonons, nanostructures, mechanical state, resistance, quantum state (including phase) and magnetic flux.

One idea is to try to keep Moore’s law going by moving it into the third dimension. Modern chips are essentially flat, but researchers are toying with chips that stack their components on top of each other. Even if the footprint of such chips stops shrinking, building up would allow their designers to keep cramming in more components, just as tower blocks can house more people in a given area than low-rise houses. Intel has incorporated the materials into new 3-D devices, called FinFETs, which have channels that pop out of the plane of the wafer.

However 3D chip would require new cooling technique as the surface area available to remove heat would grow much more slowly than the volume that generates it. One of the technology proposed is liquid cooling. Microscopic channels would be drilled into each chip, allowing cooling liquid to flow through. There are also problems with getting enough electricity and data into such a chip , he firm believes that the coolant can double as a power source. The idea is to use it as the electrolyte in a flow battery, in which electrolyte flows past fixed electrodes.

According to Semi engineering, the industry is evaluating a wide range of technologies for the sub-10nm node including gate-all-around FETs (also called nanowires), quantum well FETs, and silicon-on-insulator FinFETs.

https://www.youtube.com/watch?v=pHNIfw3el8I

Tellurium, a Rare-earth element material could produce world’s smallest transistors

Peide Ye’s lab at Purdue is one of many research groups seeking to exploit materials much thinner than silicon to achieve both smaller and higher-performing transistors. A project at Purdue University in collaboration with Michigan Technological University, Washington University in St. Louis, and the University of Texas at Dallas, found that the material, shaped like a one-dimensional DNA helix, encapsulated in a nanotube made of boron nitride, could build a field-effect transistor with a diameter of two nanometers. Transistors on the market are made of bulkier silicon and range between 10 and 20 nanometers in scale.

“This research reveals more about a promising material that could achieve faster computing with very low power consumption using these tiny transistors,” said Joe Qiu, program manager for the Army Research Office, an element of the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory, which funded this work. “That technology would have important applications for the Army.” The Army-funded research is published in the journal Nature Electronics. The Army is focused on integration, speed and precision to ensure the Army’s capability development process is adaptable and flexible enough to keep pace with the rate of technology change.

“This tellurium material is really unique. It builds a functional transistor with the potential to be the smallest in the world,” said Ye, Purdue’s Richard J. and Mary Jo Schwartz Professor of Electrical and Computer Engineering. In 2018, the same research team at Purdue discovered tellurene, a two-dimensional material derived from tellurium. They found that transistors made with this material could carry significantly more electrical current, making them more efficient.

The discovery made them curious about what else tellurium could do for transistors. The element’s ability to take the form of an ultrathin material in one dimension could help with downsizing transistors even further. One way to shrink field-effect transistors, the kind found in most electronic devices, is to build the gates that surround thinner nanowires. These nanowires are protected within nanotubes. Ye and his team worked to make tellurium as small as a single atomic chain and then build transistors with these atomic chains or ultrathin nanowires. The researchers successfully built a transistor with a tellurium nanowire encapsulated in a boron nitride nanotube. A high-quality boron nitride nanotube effectively insulates tellurium, making it possible to build a transistor.

“Silicon atoms look straight, but these tellurium atoms are like a snake. This is a very original kind of structure,” Ye said. The wiggles were the atoms strongly bonding to each other in pairs to form DNA-like helical chains, then stacking through weak forces called van der Waals interactions to form a tellurium crystal. These van der Waals interactions set apart tellurium as a more effective material for single atomic chains or one-dimensional nanowires compared with others because it’s easier to fit into a nanotube, Ye said. Because the opening of a nanotube cannot be any smaller than the size of an atom, tellurium helices of atoms could achieve smaller nanowires and, therefore, smaller transistors.

Carbon nanotube transistors

The most matured technology to take over silicon is the single-walled carbon nanotube, a rolled-up sheet of linked carbon atoms. Unlike its two-dimensional cousin, graphene, the carbon nanotube can be a natural semiconductor, which means it can be turned on and off to make a binary switch and it’s long been eyed as a potential material for speedy and energy-efficient switches. A carbon-nanotube transistor looks much the same as a silicon transistor. The main difference is that the channel is made of carbon nanotubes instead of silicon.

IBM scientists have made carbon nanotube transistors smaller and faster silicon transistors. Carbon nanotube transistors have long had the potential to be better than silicon, but this is the first time when that promise has been realized. Now IBM and others will have to scale up superior carbon nanotube devices.

But difficulties working with the material have meant that, for optimal performance, nanotube transistors have to be even larger than current silicon transistors, which are about 100 nanometers across. To cut that number down, a team of scientists used a new technique to build the contacts that draw current into and out of the carbon nanotube transistor. They constructed the contacts out of molybdenum, which can bond directly to the ends of the nanotubes, making them smaller. They also added cobalt so the bonding could take place at a lower temperature, allowing them to shrink the gap between the contacts. Another advance allowed for practical transistors. Carrying enough electrical current from one contact to another requires several nanotube “wires.” The researchers managed to lay several parallel nanotubes close together in each transistor. The total footprint of the transistor: just 40 nanometers, reported in Science. Electrical tests showed their new transistors to be faster and more efficient than ones made of silicon.

IBM scientists demonstrated a new way to shrink transistor contacts without reducing performance of carbon nanotube devices, opening a pathway to dramatically faster, smaller and more powerful computer chips beyond the capabilities of traditional semiconductors. These results could overcome contact resistance challenges all the way to the 1.8 nanometer node – four technology generations away. The success of the new method means that the ability to deliver current to carbon nanotube transistors is now independent of the length of the metal contacts, says Wilfried Haensch, who leads IBM Research’s nanotube project. It’s now clear they can make the transistors as small as necessary, he says, and this is a big step toward the company’s goal of having carbon nanotube technology ready by 2020

Spintronics

Spintronics is “A branch of physics concerned with the storage and transfer of information by means of electron spins in addition to electron charge as in conventional electronics.” Spin-based electronics focuses on devices whose functionality is based primarily on the spin degree of freedom of the carriers. This is in contrast to conventional electronics, which exploits only the charge of the carriers. Using either the spin in tandem with the charge or alone, spintronics has some advantages over conventional semiconductor electronics, including higher integration density, non-volatility, decreased power dissipation and faster processing speeds.

Nanoribbons have potential application in fabrication of spintronics. They shall be enabled by bottom up electronics, shall utilize two-dimensional (2D) Materials such as Graphene, MoS2, one-dimensional (1D) Materials such as Carbon Nanotubes and nanowires of Si, Ge, InAs, and Metal Oxides etc. Even zero-dimensional (0D) materials such as Quantum Dots and Molecular Electronics are being researches to take ultimate benefit from advantages of quantum phenomenon.

New spintronics breakthrough paves the way to faster computing

Researchers have achieved all-electric control of the spin of electrons in a major breakthrough that brings much faster and more efficient spintronics-based computation closer than ever before. Because a spinning electrically charged particle like an electron has well-known magnetic properties, the most natural way to control electronic spin is to use ferromagnetic materials embedded in spintronic devices. This, however, makes the devices very bulky, which is clearly the opposite of the direction towards which technological progress is pushing.

Led by Dr. Debray, the UC team managed to control the spin of electrons traveling on a wire with an all electrical device for the very first time, reaching a milestone in this new and very promising field that is important mainly because it allows for much smaller spintronic devices to be built.

The team used an indium arsenide “quantum point contact,” a wire only a few hundred nanometers in length whose conductivity can be modified by regulating the voltages at its two ends. The asymmetry that comes from setting two different voltages at the two ends (gates) allows the electrons to become polarized as they enter the wire.

Neuromorphic Computers

Deep Neural networks or large virtual networks of simple information-processing units, which are loosely modeled on the anatomy of the human brain have been responsible for many exciting advances in artificial intelligence in recent years.”Deep learning is useful for many applications, such as object recognition, speech, face detection,” says Vivienne Sze, the Emanuel E. Landsman Career Development Assistant Professor in MIT’s Department of Electrical Engineering and Computer Science whose group developed the new type of deep-learning chip that dramatically speeds up the ability of neural networks to process and identify data.

At the International Solid State Circuits Conference in San Francisco, MIT researchers presented a new chip designed specifically to implement neural networks. It is 10 times as efficient as a mobile GPU, so it could enable mobile devices to run powerful artificial-intelligence algorithms locally, rather than uploading data to the Internet for processing.

UK-based startup Optalysys promises optical processors for supercomputers

Optalysys has completed 320 gigaFLOP optical computer prototype, targets 9 petaFLOP product in 2017 and 17 exaFLOPS machine by 2020 .

“Optalysys’ technology applies the principles of diffractive and Fourier optics to calculate the same processor intensive mathematical functions used in CFD (Computational Fluid Dynamics) and pattern recognition,” Dr. Nick New, CEO and founder of the company explained. “Using low power lasers and high resolution liquid crystal micro-displays, calculations are performed in parallel at the speed of light,” New further described.

The Optalysys Optical Solver Supercomputer reduces energy footprint so it can be considered as eco-efficient as well. No need for special power as it only needs a standard mains power supply. As for the running cost, the super processor only costs £2,100 every year. This is way cheaper than the fast supercomputer available in China, the Tianhe-2, developed by the National University of Defense Technology. The computer alone costs $320 million but with a $21m annual running cost.

Quantum Computers

Quantum computing and quantum information processing are next revolutionary technology expected to have immense impact. Quantum computers will be able to perform tasks too hard for even the most powerful conventional supercomputer and have a host of specific applications, from code-breaking and cyber security to medical diagnostics, big data analysis and logistics. Quantum computers could accelerate the discovery of new materials, chemicals and drugs. They could dramatically reduce the current high costs and long lead times involved in developing new drugs.

Biological Computers

Recently biocomputers are becoming feasible due to advancements in nanobiotechnology. Nanobiotechnology can be defined as any type of technology that uses both nano-scale materials (i.e. materials having characteristic dimensions of 1-100 nanometers) and biologically based materials. Biocomputers use systems of biologically derived molecules—such as DNA and proteins—to perform computational calculations involving storing, retrieving, and processing data. The economical benefit of biocomputers lies in potential of all biologically derived systems to self-replicate and self-assemble into functional components given appropriate conditions.

“Without the nanoelectronics sector there would be no viable defence sector, and without defence, investment in nanoelectronics would not be feasible”, said Michael Sieber, EDA assisting one roundtable.