Advanced dual-input artificial optical synapse for recognition and generative neural network

Perovskite materials have emerged as leading candidates for optical synaptic devices due to their superior photosensitivity and tunable optoelectronic properties. However, the practical application of perovskite-based optoelectronic synaptic transistors has been hindered by issues of poor stability and high toxicity. This study developed an artificial synaptic thin film transistor (TFT) based on a Cs₂AgBiBr₆/InO_x bilayer. The device demonstrated synaptic behavior under optoelectronic hybrid stimulation (largest wavelength ∼520 nm), such as excitatory postsynaptic current (EPSC), inhibitory postsynaptic current (IPSC), paired-pulse facilitation (PPF), spike-timing-dependent plasticity (STDP), short-term memory (STM) and long-term memory (LTM). Notably, the “light writing and voltage erasing” characteristics of these devices could be utilized to construct a convolutional neural network (CNN) classifier for the CIFAR-10 dataset, demonstrating noise tolerance close to the human eye. The loss in recognition accuracy was within 1 % when Gaussian white noise and salt pepper noise were added. Furthermore, these devices exhibited great potential in cycle-consistent generative adversarial networks (CycleGAN), with the generated image quality achieving levels of 0.637 and 0.715 in improved perceptual image processing system (IPIPS) and structural similarity index measure (SSIM) evaluation metrics for the zebra dataset, indicating good image quality. This study indicates that our Cs₂AgBiBr₆-based artificial optical synaptic TFTs are promising for sensing and in-memory computing applications.