Redox transistors for neuromorphic computing

Efficiency bottlenecks inherent to conventional computing in executing neural algorithms have spurred the development of novel devices capable of “in-memory” computing. Commonly known as “memristors,” a variety of device concepts including conducting bridge, vacancy filament, phase change, and other types have been proposed as promising elements in artificial neural networks for executing inference and learning algorithms. In this article, we...