Talin A Alec, Meyer Jordan, Li Jingxian, Huang Mantao, Schwacke Miranda, Chung Heejung W, Xu Longlong, Fuller Elliot J, Li Yiyang, Yildiz Bilge
Sandia National Laboratories, Livermore, California 94551, United States.
Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Chem Rev. 2025 Feb 26;125(4):1962-2008. doi: 10.1021/acs.chemrev.4c00512. Epub 2025 Feb 17.
Non-von Neumann computing using neuromorphic systems based on analogue synaptic and neuronal elements has emerged as a potential solution to tackle the growing need for more efficient data processing, but progress toward practical systems has been stymied due to a lack of materials and devices with the appropriate attributes. Recently, solid state electrochemical ion-insertion, also known as electrochemical random access memory (ECRAM) has emerged as a promising approach to realize the needed device characteristics. ECRAM is a three terminal device that operates by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions. This mechanism can be considered as a gate-controlled bulk modulation of dopants and/or phases in the channel. Early work demonstrating that ECRAM can achieve nearly ideal analogue synaptic characteristics has sparked tremendous interest in this approach. More recently, the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered two-dimensional materials, organic and coordination polymers, and that the changes in conductance can span orders of magnitude has further attracted interest in ECRAM as the basis for analogue synaptic elements for inference accelerators as well as for dynamical devices that can emulate a wide range of neuronal characteristics for implementation in analogue spiking neural networks. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to tens of nanometers, all while achieving reliable electronic-state tuning at scaled-up rates and endurances, and with minimal energy dissipation and noise. In this review, we discuss the history, basic concepts, recent progress, as well as the challenges and opportunities for different types of ECRAM, broadly grouped by their primary mobile ionic charge carrier, including Li, protons, and oxygen vacancies.
基于模拟突触和神经元元件的神经形态系统进行的非冯·诺依曼计算,已成为应对日益增长的对更高效数据处理需求的一种潜在解决方案,但由于缺乏具有适当属性的材料和器件,向实际系统的进展受到了阻碍。最近,固态电化学离子插入,也称为电化学随机存取存储器(ECRAM),已成为实现所需器件特性的一种有前途的方法。ECRAM是一种三端器件,通过固态电化学氧化还原反应调节功能材料中的电子电导来工作。这种机制可被视为对沟道中掺杂剂和/或相的栅极控制体调制。早期证明ECRAM可实现近乎理想的模拟突触特性的工作,引发了对该方法的极大兴趣。最近,人们认识到电化学离子插入可用于调节包括过渡金属氧化物、层状二维材料、有机和配位聚合物在内的多种材料的电子特性,并且电导变化可跨越多个数量级,这进一步引起了人们对ECRAM的兴趣,它可作为推理加速器的模拟突触元件的基础,以及作为可模拟广泛神经元特性以在模拟脉冲神经网络中实现的动态器件的基础。从本质上讲,ECRAM与可充电电池有许多基本方面的共同之处,在可充电电池中,离子插入材料因其可逆存储电荷和能量的能力而被广泛使用。然而,计算应用提出了截然不同的要求:系统将需要数百万个器件,缩小到几十纳米,同时要以放大的速率和耐久性实现可靠的电子态调节,并且能量耗散和噪声最小。在这篇综述中,我们讨论了不同类型ECRAM的历史、基本概念、最新进展以及挑战和机遇,这些类型大致按其主要移动离子电荷载体进行分组,包括锂、质子和氧空位。