Highly Efficient Invisible TaO_x/ZTO Bilayer Memristor for Neuromorphic Computing and Image Sensing

In today's new era, multifunctional devices are most prominent due to their compact design, reduction in operating cost, and reduced need of being limited to single functional devices. The electronic synapses and electrooptic functions of the device are such a cornerstone for neuromorphic computing and image sensing applications. In this work, we fabricate a ZTO-based invisible memristor for simulating the human brain for neuromorphic computing and image sensing applications. Long-term potentiation and depression-at least 790-repetitive cycles are observed which ensures the synaptic strength. The first-principles density functional theory calculations give insights into the device's microscopic charge density distribution and switching mechanism. The experimental potentiation and depression data are used to train the Hopfield neural network (HNN) for image recognition of 28 × 28 pixels comprising 784 synapses. The HNN can be successfully trained to identify the input image with a training accuracy of more than 96% in 17 iterations. Furthermore, the device shows excellent highly stable electrical set and optical reset endurance for at least 1500 cycles without degradation, good retention (10⁴ s) at 90 °C, and high transparency (∼85%). This work not only enables us to use our device in artificial intelligence but also provides a significant advantage in the field of image sensing.