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用于人工智能的可重构钙钛矿镍酸盐电子学。

Reconfigurable perovskite nickelate electronics for artificial intelligence.

机构信息

School of Materials Engineering, Purdue University, West Lafayette, IN 47907, USA.

Department of Electrical Engineering, Pennsylvania State University, University Park, PA, 16802, USA.

出版信息

Science. 2022 Feb 4;375(6580):533-539. doi: 10.1126/science.abj7943. Epub 2022 Feb 3.

Abstract

Reconfigurable devices offer the ability to program electronic circuits on demand. In this work, we demonstrated on-demand creation of artificial neurons, synapses, and memory capacitors in post-fabricated perovskite NdNiO devices that can be simply reconfigured for a specific purpose by single-shot electric pulses. The sensitivity of electronic properties of perovskite nickelates to the local distribution of hydrogen ions enabled these results. With experimental data from our memory capacitors, simulation results of a reservoir computing framework showed excellent performance for tasks such as digit recognition and classification of electrocardiogram heartbeat activity. Using our reconfigurable artificial neurons and synapses, simulated dynamic networks outperformed static networks for incremental learning scenarios. The ability to fashion the building blocks of brain-inspired computers on demand opens up new directions in adaptive networks.

摘要

可重构设备提供了按需编程电子电路的能力。在这项工作中,我们展示了在钙钛矿 NdNiO 器件制造后按需创建人工神经元、突触和存储电容器的能力,这些器件可以通过单次电脉冲简单地重新配置为特定用途。钙钛矿镍酸盐的电子特性对氢离子局部分布的敏感性使得这些结果成为可能。利用我们的存储电容器的实验数据,储层计算框架的模拟结果表明,在数字识别和心电图心跳活动分类等任务中表现出优异的性能。使用我们的可重构人工神经元和突触,模拟动态网络在增量学习场景中优于静态网络。按需塑造脑启发计算机构建模块的能力为自适应网络开辟了新的方向。

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