Gate-Tunable Neuromorphic Devices Enabled by Two-Dimensional Materials

被引：1
|
作者：
Hersam, Mark C. [1 ]
机构：
[1] Northwestern Univ, Evanston, IL 60208 USA
来源：
6TH IEEE ELECTRON DEVICES TECHNOLOGY AND MANUFACTURING CONFERENCE (EDTM 2022) | 2022年
基金：
美国国家科学基金会;
关键词：
neuromorphic computing; 2D materials; van der Waals heterojunctions; Gaussian transistors; memtransistors; continuous learning; MEMTRANSISTORS;
D O I：
10.1109/EDTM53872.2022.9798164
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Neuromorphic (i.e., brain-like) computing aims to circumvent the limitations of von Neumann architectures by spatially co-locating processor and memory blocks or even combining logic and data storage functions within the same device. Neuromorphic devices also have the potential to provide efficient architectures for image recognition, machine learning, and artificial intelligence. With this motivation in mind, this paper will explore how the unique materials properties of two-dimensional (2D) materials enable opportunities for novel gate-tunable neuromorphic devices.
引用
收藏
页码：154 / 156
页数：3
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