The enormous energy consumption of modern machine learning technologies, such as deep learning and generative artificial intelligence, is one of the most critical concerns of the time. To solve this problem, physical reservoir computing, which uses the non-linear dynamics exhibited by mechanical systems such as materials and devices as a computational resource for highly efficient information processing, has attracted much attention in recent years. In particular, ion-gated transistors, a group of devices that control electrical conductivity using electrochemical mechanisms such as electric double layers and redox, show very high computational performance with complex and diverse output properties in contrast to their simple structures, due to the complexity of the physical and chemical processes involved. This research provides an overview of physical reservoir computing using ion-gating transistors, focusing on the materials used, various computational tasks, and operating mechanisms.

中文翻译：

---

利用在双电层和氧化还原机制中运行的离子门控晶体管进行物理储层计算


深度学习和生成式人工智能等现代机器学习技术的巨大能源消耗是当今最关键的问题之一。为了解决这个问题，物理储层计算利用材料和器件等机械系统所表现出的非线性动力学作为高效信息处理的计算资源，近年来引起了人们的广泛关注。特别是，离子门控晶体管，一组使用双电层和氧化还原等电化学机制控制导电性的器件，由于涉及的物理和化学过程的复杂性，与其简单的结构相比，显示出非常高的计算性能和复杂多样的输出特性。本研究概述了使用离子门控晶体管的物理储层计算，重点介绍了使用的材料、各种计算任务和操作机制。