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EDFA Technical Articles

Superconducting Electronics: A New Frontier for Failure Analysis and Reliability

Nancy A. Missert

Published May 1, 2019• 0 citations
Publisher

Abstract

Abstract This column assesses the current state and outlook for superconducting device technology and its application in exascale computing.
DOI
10.31399/asm.edfa.2019-2.p054

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Supercomputers are making rapid progress. For the world's fastest supercomputer “Frontier,” the computing speed has reached 10 trillion floating-point instruction cycles per second (1018, exaFLOPS), making many existing complex problems, including weather forecasting and disaster prevention, more accurate than ever. However, the calculation of real systems with a large number of electrons is still difficult. Exascale computers can simulate some systems with dual quantum states, but in the case ofndual quantum states, the exponential growth of 2nmakes the calculation to simulate real system impossible. If we want to understand the dynamic quantum state evolution of a real matter, it is even more difficult to track the dynamics of “every possible” configuration of a huge number of 2nquantum states. If n is 20, it will take about 128 KB of memory to store with the current computer's memory, which can be done by ordinary computers. When there are hundreds of electrons, the memory required to store this system exceeds the number of particles in the entire universe. Unfortunately, almost all materials and drugs have at least hundreds of electrons, which means that direct use of Exascale computers to develop and design any new drugs and materials also becomes impossible. At the same time, after the popularization of artificial intelligence and deep learning, it is also found that the computing power, bandwidth, and memory space of classical computers are not enough, while quantum computing has at least the following three advantages and has the opportunity to provide solutions for complex systems.

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Superconducting digital electronics, especially Single Flux Quantum (SFQ), has emerged as a promising beyond-CMOS technology with Josephson junctions (JJ) as the active device. It has the potential to meet the booming demands of lower power consumption and higher operation speeds in the electronics industry and future exascale supercomputing systems. Despite these promises, scaling SFQ circuits remains a serious challenge that motivates the support of multiple SFQ clock domains. Towards this end, this paper analyzes the impact of setup time violations and metastability in SFQ circuits comparing the derived analytical models to their CMOS counterparts. It also proposes new techniques to reduce the average latency in metastability-tolerant SFQ synchronizers, and evaluates their effects on the layout and critical margin of the design. It further extends the proposed model to estimate the Mean Time Between Failure (MTBF) of flip-flop-based synchronizers and shows that their MTBF with the current feature sizes is unaffected by noise, similar to CMOS. Finally, it curve fits this model to simulations using the state-of-the-art SFQ5ee process and shows that a two-flop SFQ synchronizer with a clock frequency of 25 GHz has an estimated MTBF of ~106years.
Quantum technologies have matured in a way that real applications are considered to be viable. The new quality emerges from the use of explicit quantum phenomena in single objects as qubits for computation, or photons for sensing and communication. However, to make these features exploitable, suitable microelectronic components for controlling and readout of the quantum states have to be available. For this, superconductive solid-state electronic circuits are considered to be promising candidates. First demonstrations for suitability are known. In order to provide scaling towards large-scale integrated structures, appropriate design methods and capabilities have to be developed. We provide an assessment of the current state of design automation for such superconducting digital electronic structures and survey existing approaches and tools.
Supercomputers are making rapid progress. For the world's fastest supercomputer “Frontier,” the computing speed has reached 10 trillion floating-point instruction cycles per second (1018, exaFLOPS), making many existing complex problems, including weather forecasting and disaster prevention, more accurate than ever. However, the calculation of real systems with a large number of electrons is still difficult. Exascale computers can simulate some systems with dual quantum states, but in the case ofndual quantum states, the exponential growth of 2nmakes the calculation to simulate real system impossible. If we want to understand the dynamic quantum state evolution of a real matter, it is even more difficult to track the dynamics of “every possible” configuration of a huge number of 2nquantum states. If n is 20, it will take about 128 KB of memory to store with the current computer's memory, which can be done by ordinary computers. When there are hundreds of electrons, the memory required to store this system exceeds the number of particles in the entire universe. Unfortunately, almost all materials and drugs have at least hundreds of electrons, which means that direct use of Exascale computers to develop and design any new drugs and materials also becomes impossible. At the same time, after the popularization of artificial intelligence and deep learning, it is also found that the computing power, bandwidth, and memory space of classical computers are not enough, while quantum computing has at least the following three advantages and has the opportunity to provide solutions for complex systems.