“SPOOKY PHOTONS” MAY BREAK MINIATURIZATION BARRIER

September 29, 2000

SCIENCE & ENGINEERING NEWS

Pasadena, CALIF. — Every year, faster and faster computers become available. The upcoming holiday season will be no exception, with some chip speeds already being advertised in terms of gigahertz, a thousand times faster than the more familiar yardstick of megahertz. We take for granted Moore’s law, the idea that computers double their speed every 18 months or so. Will this ever end?

Many experts believe the end is in sight. At some point, traditional manufacturing procedures will hit a wall on the road to faster chips. But now, physicists may have found a way to help researchers go past this dead end. In a paper published in the September 25 issue of the journal Physical Review Letters, researchers show that a special kind of light may eventually enable manufacturers to continue miniaturizing – and thereby speeding up – computer chips and other electronic devices well beyond what traditional techniques allow. Yet, their technique would still retain the same basic manufacturing approach, known as lithography, which uses light to sculpt the components of computer chips.

A computer chip is basically a grid of on-off switches connected to each other. A state-of-the-art chip contains millions of these switches, known as transistors. Electrical current flows through these switches in order to perform the calculations needed to crunch numbers in spreadsheets, write letters on wordprocessors, and zap aliens in computer games. How do manufacturers design faster chips? Generally, they keep shrinking the transistors to smaller and smaller sizes – allowing chipmakers to crowd together more transistors in a tinier area. This in turn means that electric current travels smaller distances through the transistors – resulting in faster processing speeds.

Currently, state-of-the-art computer chips have transistors with dimensions between roughly 180 and 220 nanometers (nm) – only about 2000 atoms wide, or about 400 times narrower than the width of a human hair. Traditional computers as we know them can function with chips having dimensions as small as 25 nm – about 250 atoms wide, or about 3000 times narrower than a human hair width. At that point, bizarre effects of the subatomic world come in, messing up the calculations of traditional computers.

But researchers have become worried that we won’t even be able to reach this 25 nm limit. What is the problem?

The roadblock comes at one of the earliest steps of chip manufacture. In lithography, one first shines light on a photosensitive material to create a stencil-like “mask.” Placing this mask over a block of material such as silicon, manufacturers can carve or “etch” the components that make up transistors and other electronic devices. However, chipmakers can only endow transistors and devices with dimensions as small as those on the mask. The roughly 180-220 nm features on state-of-the-art chips originate from similar dimensions on the mask.

What determines the dimensions on lithographic masks is the behavior of light. A light beam can be visualized as a rippling wave with crests and valleys. The distance between successive crests is called the wavelength. Like a water wave passing between two rocks, a light wave can split up. Just as it happens for water continuing beyond the rocks, the waves can later recombine. In the process of recombining, it can create wave patterns smaller than its very own wavelength.

But a central principle of optics–known as the “Rayleigh criterion” – says that a light wave can’t make patterns with features smaller than half its wavelength. In fact, the Rayleigh criterion says that 248-nm-wavelength “deep ultraviolet” light – currently used to make the chips with the approximately 180-220 nm dimensions – can’t create chips with features smaller than 124 nm. Smaller features are possible by using shorter-wavelength light, but such light gets more and more difficult to produce as you go to shorter wavelengths.

In new research by Jonathan Dowling of JPL/Caltech and his colleagues, physicists illustrate that the Rayleigh criterion is mainly a limit of classical, pre-20th century physics–and not of the “quantum” physics discovered and explored since the 20th century. This research – still a theoretical proposal at this stage – is made by a team of physicists at the Jet Propulsion Laboratory, the California Institute of Technology, and the University of Wales, Bangor. Their proposal is based on earlier insights and results by physicists at many other institutions, including UCLA, the University of Rochester, Boston University, and the University of Maryland, and the Federal University of Minas Gerais in Brazil.

Dowling and his colleagues show that existing sources of light can potentially make chips with dimensions that are much smaller fractions of the wavelength than classical physics allows. In their scenario, 248-nm light could make features as tiny as 62 nm – a fourth of the wavelength – or potentially much smaller–through a quantum physics process known as “entanglement.”

To understand entanglement, it’s useful to temporarily visualize a light beam not as a wave, but as a stream of particles called photons. In this “particle” picture of light, photons are usually unaffected by one another – each photon normally behaves independently of its neighbors. But sometimes two or more photons can become interlinked or “entangled” – whereby the properties of one photon are dependent upon the properties of its partners. As physicists like to say, entangled photons are “correlated” with each other. Albert Einstein called this process “spooky action at a distance” because the particles can seem to influence each other instantly, even if they become separated by the distance of a galaxy or more! In the laboratory, entangled photons can be produced by passing a light beam through a special crystal.

The entangled photons come into play in the researchers’ proposal for “quantum interferometric optical lithography,” an exotic version of lithography that takes advantage of the unique properties of the quantum world. In their proposal, two entangled photons enter a setup with a pair of paths. The photons travel as a single unit. However, the setup is designed so that it is impossible to determine if the two-photon unit takes the first path or the second path. This very property makes the photon pair behave once again as a single rippling wave. This wave splits up to travel both paths. Eventually, the two parts of the wave are made to recombine on a surface. Because the two photons constituting the light wave are entangled with each other, and therefore are correlated in a special way, they create patterns equivalent to those made by a single photon with half the wavelength.

Therefore, an entangled pair of photons each with 500 nm wavelength would act as a single photon with 250 nm – allowing researchers to write 125 nm patterns on a side, two times smaller than the Rayleigh criterion allows for a single “classical” photon with a 500-nm wavelength. Such an entangled pair could write circuit patterns with four times smaller area, since the surface of a mask has two dimensions, length and width, and there is a twofold improvement on each side. Preparing a trio of entangled photons – a difficult task – and sending them through the device would create even better results: they would act as a single photon with a third of the wavelength, enabling patterns with nine-fold smaller area on a chip. Entangling four entangled photons – more difficult yet – could produce patterns with a 16-fold smaller area, and so on.

To realize this proposal, researchers need to surmount numerous technical challenges. Towards these ends, scientists are developing “two-photon resists,” materials designed to absorb photon pairs arriving simultaneously. But, for example, they have yet to develop the special materials required to generate entangled photons at short wavelengths. Still, physicists are already working on demonstrating simple versions of this proposal. One of them is Yanhua Shih, a professor of physics at the University of Maryland in Baltimore County.

“My laboratory is working on this new idea of optical lithography experimentally,” says Shih. “Jon Dowling and his co-workers did not only propose a new way of conducting lithography in this paper,” according to Shih, who has done related experiments on entanglement. “The fundamental idea is far more important, in my opinion. It is a great idea to utilize this very important physics to the application of lithography.”

“I am impressed at the very clever application of some very fundamental features of the quantum mechanics of the electromagnetic field,” comments Carlos Stroud, a professor of optics and physics at the University of Rochester. “That said, there would appear to be rather substantial engineering problems before we get super-dense computer chips. These engineering problems may be a lot tougher than the quantum problem. Still, it clearly demonstrates the advances that are available when technology really is able to take advantage of quantum coherence. It is a nice step in that direction.”

The new proposal opens the possibility of using light at existing wavelengths to manufacture computer chips smaller than 25 nm, the size limit below which classical computer designs begin to fail. “In classical computing, these quantum effects are viewed as bad,” says Dowling of JPL/Caltech. “However, we embrace these quantum effects and exploit them,” he says. Such effects can lead to interesting new electronic devices taking advantage of processes in the quantum realm.

============================================================

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire