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Quantum computational advantage using photons.利用光子实现量子计算优势。
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Inference in artificial intelligence with deep optics and photonics.人工智能中的深度学习光学与光子学推理。
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An integrated optical modulator operating at cryogenic temperatures.一种在低温下工作的集成光学调制器。
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Broadband transparent optical phase change materials for high-performance nonvolatile photonics.宽带透明光致相变材料用于高性能非易失性光子学。
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In-memory computing on a photonic platform.光子平台上的内存计算。
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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.一种通过自我对弈掌握国际象棋、将棋和围棋的通用强化学习算法。
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用于应急硅集成光学计算的材料。

Materials for emergent silicon-integrated optical computing.

作者信息

Demkov Alexander A, Bajaj Chandrajit, Ekerdt John G, Palmstrøm Chris J, Ben Yoo S J

机构信息

Department of Physics, The University of Texas, Austin, Texas 78712, USA.

Department of Computer Science, The University of Texas, Austin, Texas 78712, USA.

出版信息

J Appl Phys. 2021 Aug 21;130(7):070907. doi: 10.1063/5.0056441. Epub 2021 Aug 19.

DOI:10.1063/5.0056441
PMID:34483360
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8378901/
Abstract

Progress in computing architectures is approaching a paradigm shift: traditional computing based on digital complementary metal-oxide semiconductor technology is nearing physical limits in terms of miniaturization, speed, and, especially, power consumption. Consequently, alternative approaches are under investigation. One of the most promising is based on a "brain-like" or scheme. Another approach is quantum computing using photons. Both of these approaches can be realized using silicon photonics, and at the heart of both technologies is an efficient, ultra-low power broad band optical modulator. As silicon modulators suffer from relatively high power consumption, materials other than silicon itself have to be considered for the modulator. In this Perspective, we present our view on such materials. We focus on oxides showing a strong linear electro-optic effect that can also be integrated with Si, thus capitalizing on new materials to enable the devices and circuit architectures that exploit shifting computational machine learning paradigms, while leveraging current manufacturing infrastructure. This is expected to result in a new generation of computers that consume less power and possess a larger bandwidth.

摘要

计算架构的进展正接近一种范式转变

基于数字互补金属氧化物半导体技术的传统计算在小型化、速度,尤其是功耗方面正接近物理极限。因此,正在研究替代方法。最有前景的方法之一是基于“类脑”或 方案。另一种方法是使用光子的量子计算。这两种方法都可以通过硅光子学实现,并且这两种技术的核心都是高效、超低功耗的宽带光调制器。由于硅调制器存在相对较高的功耗,因此必须考虑使用硅本身以外的材料来制造调制器。在这篇观点文章中,我们阐述了对这类材料的看法。我们关注那些具有强线性电光效应且能与硅集成的氧化物,从而利用新材料来实现利用不断变化的计算机器学习范式的器件和电路架构,同时借助现有的制造基础设施。这有望带来新一代功耗更低、带宽更大的计算机。