In-sensor computing with halide perovskite-based optoelectronic reservoir networks

Physical reservoir computing can provide efficient neuromorphic in- and near-sensor computing applications. Typically, reservoir networks are designed to process light or voltage inputs. Here, we demonstrate a multimodal optoelectronic reservoir network based on halide perovskite semiconductor devices capable of processing voltage and light inputs, which is also scalable for constructing high-density sensor arrays. The devices consist of micrometer-sized, asymmetric crossbars covered with a methylammonium lead iodide (MAPbI 3 ) perovskite film. Using 4-bit inputs and linear readout layers for classification, we demonstrate multimodal networks capable of processing both voltage and light inputs. The networks reach mean accuracies up to 95.3% ± 0.1% and 87.8% ± 0.1% for image and video classification, respectively. The networks significantly outperformed linear classifier references by 3.1% for images and 14.6% for video. We show that longer retention times benefit classification accuracy for single-mode networks and give guidelines for choosing optimal experimental parameters.