利用薄膜设备中的铁有序性将模拟存储器和神经形态计算应用降至深冷温度

IF 4.1 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Sayani Majumdar
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引用次数: 0

摘要

超越冯-诺依曼时代的未来计算在很大程度上依赖于新兴设备,这些设备可以广泛利用材料和设备物理来实现新功能,并能为人工智能(AI)任务提供高能效的实时计算。此外,类脑计算要求在实用电路中大规模集成突触和神经元,这就需要纳米技术来支持这一硬件开发,而所有这些都应以可承受的工艺复杂度和成本来实现,以便尽快将解决方案推向市场。要使人工智能更接近量子计算和空间技术,还需要在低温和抗辐射条件下运行。考虑到所有这些要求,利用铁氧体排序的纳米电子器件已成为一种有前途的替代方案。本综述讨论了将自旋电子和铁电器件集成到神经形态和模拟存储器应用中的基本架构、铁磁和铁电域结构及其动态控制以实现可靠的多位存储器操作、突触和神经元漏电整合与发射(LIF)功能,最后讨论了大规模集成的可能性、挑战和未来研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Harnessing ferroic ordering in thin film devices for analog memory and neuromorphic computing applications down to deep cryogenic temperatures
The future computing beyond von Neumann era relies heavily on emerging devices that can extensively harness material and device physics to bring novel functionalities and can perform power-efficient and real time computing for artificial intelligence (AI) tasks. Additionally, brain-like computing demands large scale integration of synapses and neurons in practical circuits that requires the nanotechnology to support this hardware development, and all these should come at an affordable process complexity and cost to bring the solutions close to market rather soon. For bringing AI closer to quantum computing and space technologies, additional requirements are operation at cryogenic temperatures and radiation hardening. Considering all these requirements, nanoelectronic devices utilizing ferroic ordering has emerged as one promising alternative. The current review discusses the basic architectures of spintronic and ferroelectric devices for their integration in neuromorphic and analog memory applications, ferromagnetic and ferroelectric domain structures and control of their dynamics for reliable multibit memory operation, synaptic and neuronal leaky-integrate-and-fire (LIF) functions, concluding with their large-scale integration possibilities, challenges and future research directions.
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来源期刊
Frontiers in Nanotechnology
Frontiers in Nanotechnology Engineering-Electrical and Electronic Engineering
CiteScore
7.10
自引率
0.00%
发文量
96
审稿时长
13 weeks
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