Opto-electronic memristors: Prospects and challenges in neuromorphic computing

被引：40

作者：

Emboras, Alexandros ^{[1
]}

Alabastri, Alessandro ^{[2
]}

Lehmann, Paul ^{[1
]}

Portner, Kevin ^{[1
]}

Weilenmann, Christoph ^{[1
]}

Ma, Ping ^{[3
]}

Cheng, Bojun ^{[3
]}

Lewerenz, Mila ^{[3
]}

Passerini, Elias ^{[3
]}

Koch, Ueli ^{[3
]}

Aeschlimann, Jan ^{[1
]}

Ducry, Fabian ^{[1
]}

Leuthold, Juerg ^{[3
]}

Luisier, Mathieu ^{[1
]}

机构：

[1] Swiss Fed Inst Technol, Integrated Syst Lab IIS, CH-8092 Zurich, Switzerland

[2] Rice Univ, Dept Elect & Comp Engn, Houston, TX 77005 USA

[3] Swiss Fed Inst Technol, Inst Electromagnet Fields IEF, CH-8092 Zurich, Switzerland

来源：

APPLIED PHYSICS LETTERS | 2020年 / 117卷 / 23期

关键词：

NEURAL-NETWORKS; MEMORY; CLASSIFICATION;

D O I：

10.1063/5.0028539

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

Memristive-based electro-optical neuromorphic hardware takes advantage of both the high-density of electronic circuits and the high bandwidth of their photonic counterparts, thus showing potential for low-power artificial intelligence applications. In this Perspective paper, we introduce a class of electro-optical memristors that can emulate the key properties of synapses and neurons, which are essential features for the realization of electro-optical neuromorphic functionalities. We then describe the challenges associated with existing technologies and finally give our viewpoint on possible developments toward an energy-efficient neuromorphic platform.

引用

页数：8

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