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Topical Review | Open Access

Nano device fabrication for in-memory and in-sensor reservoir computing

Yinan Lin1,2,7, Xi Chen3,4,7, Qianyu Zhang1,2, Junqi You1,2, Renjing Xu6, Zhongrui Wang4,5(), Linfeng Sun1,2 ()
Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
Beijing Key Laboratory of Nanophotonics & Ultrafine Optoelectronic Systems, School of Physics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region of China, People's Republic of China
ACCESS—AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong Special Administrative Region of China, People's Republic of China
School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, People's Republic of China
Thrust of Microelectronics of Function Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, People's Republic of China

7These authors contributed equally.

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Abstract

Recurrent neural networks (RNNs) have proven to be indispensable for processing sequential and temporal data, with extensive applications in language modeling, text generation, machine translation, and time-series forecasting. Despite their versatility, RNNs are frequently beset by significant training expenses and slow convergence times, which impinge upon their deployment in edge AI applications. Reservoir computing (RC), a specialized RNN variant, is attracting increased attention as a cost-effective alternative for processing temporal and sequential data at the edge. RC's distinctive advantage stems from its compatibility with emerging memristive hardware, which leverages the energy efficiency and reduced footprint of analog in-memory and in-sensor computing, offering a streamlined and energy-efficient solution. This review offers a comprehensive explanation of RC's underlying principles, fabrication processes, and surveys recent progress in nano-memristive device based RC systems from the viewpoints of in-memory and in-sensor RC function. It covers a spectrum of memristive device, from established oxide-based memristive device to cutting-edge material science developments, providing readers with a lucid understanding of RC's hardware implementation and fostering innovative designs for in-sensor RC systems. Lastly, we identify prevailing challenges and suggest viable solutions, paving the way for future advancements in in-sensor RC technology.

Keywords

reservoir computingmemristive device fabricationcompute-in-memoryin-sensor computing

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International Journal of Extreme Manufacturing
Volume 7 Issue 1,
February 2025
DOI: 10.1088/2631-7990/ad88bb
Cite this article:
Lin Y, Chen X, Zhang Q, et al. Nano device fabrication for in-memory and in-sensor reservoir computing. International Journal of Extreme Manufacturing, 2025, 7(1). https://doi.org/10.1088/2631-7990/ad88bb
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