LAUSR

The recent advances in optic neuromorphic devices have led to a subsequent rise in use for construction of energy-efficient artificial vision systems. The widespread use can be attributed to their ability to capture, store, and process visual information from the environment. The primary limitations of existing optic neuromorphic devices include nonlinear weight updates, cross-talk issues, and silicon process incompatibility. In this study, we experimentally demonstrate a highly-linear, light-tunable, cross-talk-free, and silicon-compatible one-phototransistor-one-memristor (1PT1R) optic memristor for the implementation of optic artificial neural networks (OANN). For optic image recognition in the experiment an OANN was constructed using a 16×3 1PT1R memristor array, and it was trained on an online platform. The model yielded an accuracy of 99.3% after only 10 training epochs. The 1PT1R memristor, which shows good performance, demonstrated its ability as an excellent hardware solution for highly efficient optic neuromorphic and edge computing. This article is protected by copyright. All rights reserved.