Materials Science Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, United States of America.
Department of Chemistry, Colorado School of Mines, 1500 Illinois Avenue, Golden, CO 80401, United States of America.
Nanotechnology. 2021 Jun 22;32(37). doi: 10.1088/1361-6528/abfa51.
The approaching end of Moore's law scaling has significantly accelerated multiple fields of research including neuromorphic-, quantum-, and photonic computing, each of which possesses unique benefits unobtained through conventional binary computers. One of the most compelling arguments for neuromorphic computing systems is power consumption, noting that computations made in the human brain are approximately 10times more efficient than conventional CMOS logic. This review article focuses on the materials science and physical mechanisms found in metal chalcogenides that are currently being explored for use in neuromorphic applications. We begin by reviewing the key biological signal generation and transduction mechanisms within neuronal components of mammalian brains and subsequently compare with observed experimental measurements in chalcogenides. With robustness and energy efficiency in mind, we will focus on short-range mechanisms such as structural phase changes and correlated electron systems that can be driven by low-energy stimuli, such as temperature or electric field. We aim to highlight fundamental materials research and existing gaps that need to be overcome to enable further integration or advancement of metal chalcogenides for neuromorphic systems.
摩尔定律的缩限逼近极大地加速了多个研究领域的发展，包括神经形态学、量子和光子计算，这些领域各自具有通过传统二进制计算机无法获得的独特优势。神经形态计算系统最引人注目的论据之一是功耗，指出人脑进行的计算比传统的 CMOS 逻辑大约高效 10 倍。本文综述了目前正在探索用于神经形态应用的金属硫族化合物中的材料科学和物理机制。我们首先回顾了哺乳动物大脑神经元成分中的关键生物信号产生和转导机制，随后将其与硫族化合物中的观察到的实验测量进行了比较。考虑到稳健性和能效，我们将重点关注短程机制，如结构相变和相关电子系统，这些机制可以由低能刺激（如温度或电场）驱动。我们旨在强调基础材料研究和需要克服的现有差距，以使金属硫族化合物能够进一步集成或推进神经形态系统。
