有机神经形态器件的材料策略

IF 10.6 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Aristide Gumyusenge, A. Melianas, S. Keene, A. Salleo
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引用次数: 25

摘要

随着人工智能(AI)逐渐促进人与机器之间的无缝交互,神经形态计算正变得越来越突出。传统的冯诺依曼架构和互补的金属氧化物半导体晶体管缩放无法满足人工智能对计算密度和能效的高要求。神经形态计算旨在通过使用类脑计算架构和新型突触记忆来解决这些挑战,这些突触记忆可以共同分配信息存储和计算,从而在高能效和高内存密度下实现低延迟。尽管各种新兴的存储设备已经被广泛研究以模拟生物突触的功能,但目前还没有材料/设备系统既包含高性能神经形态计算所需的指标,又包含潜在的身体-计算机集成所需的生物相容性。在这篇综述中,我们旨在为读者提供实现高性能有机神经形态器件的一般设计原理和材料要求。我们从最近的文献中使用指导性的例子来讨论每个要求,说明挑战以及未来的研究机会。尽管有机设备在成为神经形态计算的主要参与者方面仍然面临许多挑战,主要是由于它们缺乏与数字逻辑集成所需的后端过程的遵从性,但我们认为它们的生物相容性和机械一致性使它们在创建自适应生物接口,脑机接口和生物学启发的假肢方面具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Materials Strategies for Organic Neuromorphic Devices
Neuromorphic computing is becoming increasingly prominent as artificial intelligence (AI) facilitates progressively seamless interaction between humans and machines. The conventional von Neumann architecture and complementary metal-oxide-semiconductor transistor scaling are unable to meet the highly demanding computational density and energy efficiency requirements of AI. Neuromorphic computing aims to address these challenges by using brain-like computing architectures and novel synaptic memories that coallocate information storage and computation, thereby enabling low latency at high energy efficiency and high memory density. Though various emerging memory devices have been extensively studied to emulate the functionality of biological synapses, there is currently no material/device system that encompasses both the needed metrics for high-performance neuromorphic computing and the required biocompatibility for potential body-computer integration. In this review, we aim to equip the reader with general design principles and materials requirements for realizing high-performance organic neuromorphic devices. We use instructive examples from recent literature to discuss each requirement, illustrating the challenges as well as future research opportunities. Though organic devices still face many challenges to become major players in neuromorphic computing, mostly due to their lack of compliance with back-end-of-line processes required for integration with digital logic, we propose that their biocompatibility and mechanical conformability give them an advantage for creating adaptive biointerfaces, brain-machine interfaces, and biology-inspired prosthetics.
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来源期刊
Annual Review of Materials Research
Annual Review of Materials Research 工程技术-材料科学:综合
CiteScore
17.70
自引率
1.00%
发文量
21
期刊介绍: The Annual Review of Materials Research, published since 1971, is a journal that covers significant developments in the field of materials research. It includes original methodologies, materials phenomena, material systems, and special keynote topics. The current volume of the journal has been converted from gated to open access through Annual Reviews' Subscribe to Open program, with all articles published under a CC BY license. The journal defines its scope as encompassing significant developments in materials science, including methodologies for studying materials and materials phenomena. It is indexed and abstracted in various databases, such as Scopus, Science Citation Index Expanded, Civil Engineering Abstracts, INSPEC, and Academic Search, among others.
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