All-In-One Hardware Devices with Event-Based Vision Sensor Arrays for Image Sensing, Computing, and Learning

Metal oxide semiconductors (MOSs) are considered as potential candidates for the low-cost, large-area fabrication of flexible optoelectronic devices. However, the current optoelectronic devices based on MOSs are limited to unidirectional photoresponse, which constrains the performance of MOSs-based vision sensors for artificial vision systems. Herein, for the first time, a flexible artificial vision system integrated with optical perception, computation, and learning functionalities is demonstrated using SnO optoelectronic synaptic transistor-based event-driven vision sensors to enable dynamic image perception, noise reduction, detection, and recognition. Specifically, an ambipolar SnO transistor is demonstrated by introducing HfO₂ passivation layer, which facilitates the movement of O atoms around Sn-vacancy sites to the HfO₂ layer to achieve the transformation from p-type to ambipolar transport behaviors. More importantly, the HfO₂-passivated SnO transistors exhibit gate-tunable bidirectional photoresponse behavior, which is essential to simulate the neurobiological functionalities of bipolar cells. This way, the multilayer neural network learning circuit built from SnO transistors achieves fast recognition at a 16% Gaussian noise level and high recognition accuracy up to 95.2% for pattern letters. Under the bending states, recognition accuracies are still retained at 91.2%. These properties are well retained even under the influence of 100% offset of the synaptic programming value.