Miniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single‐element image sensors that allow reception of information and execution of in‐memory computing processes while maintaining memory for much longer durations without the need for frequent electrical or optical rehearsals. In this work, ultra‐thin (<3 nm) doped indium oxide (In2O3) layers are engineered to demonstrate a monolithic two‐terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endows features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for UV spectrum based proof‐of‐concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real‐time, in‐sensor memorization, and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand.

The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/admt.202201772.

Optoelectronic devices based on optically responsive materials have gained significant attention due to their low cross talk and reduced power consumption. These devices rely on light‐induced changes in conductance states, which are used to create synaptic weights for image recognition tasks in neural networks. However, a major drawback of such devices is the rapid decay of conductance states after light stimulus removal, which hinders their long‐term memory and performance without a continuous external stimulus in place. To address this issue, a platform neural network scheme is proposed to counter the natural decay of conductance in optoelectronic devices. The approach restores the memory effect of the devices and significantly enhances their performance by several orders of magnitude without using additional energy‐intensive techniques like training pulses or gate fields. Herein, the model is validated experimentally using optoelectronic devices fabricated with two different materials, BP and doped In2O3, and demonstrates the restoration of memory/image retention ability to any material system being studied for optoelectronic synapses and vision. This approach has important implications for the practical application of neuromorphic visual processing technologies, bringing them closer to real‐world applications.