Halide Perovskite Photovoltaics for In-Sensor Reservoir Computing

As intelligent electronics get laden with multimodal sensors, the data transfer and computation increase their energy expenditure. Consequently, researchers aim to develop efficient computing paradigms or integrate energy harvesting from ambient sources. Halide perovskites possess unique photophysics and coupled ionic-electronic dynamics that actualize memory devices for brain-inspired computing. Synergising the computing capability with their conventional light harvesting efficacy could aptly address the aforementioned problem. In a novel approach, the <b>transient open circuit voltage (V_OC) of a methylammonium lead bromide-based solar cell is exploited to serve as a volatile and versatile short-term memory for optoelectronic in-sensor reservoir computing</b>. The proposed approach using photovoltaics for reservoir computing could self-power the optoelectronic memory element by innately generating voltages in response to light as opposed to photomemristors which require a read operation to probe the conductance state. Operating in open circuit conditions ensures<b> extremely low power consumption (&lt;1pW)</b>. Drift diffusion simulations were performed to unravel how the entangled ionic-electronic processes manifest into short-term memory in the V_OCof halide perovskite photovoltaics. Based on this understanding, the recombination kinetics of could be engineered to modulate the timescales from 0.1ms to 100ms. The superior reservoir computing properties were validated by characterizing the echo-state properties and performing benchmarking task of image recognition with <b>highly reproducible (std. dev. = 0.12%) and robust (endurance &gt; 10000 cycles) transformation of optical inputs into unique reservoir states</b>. To demonstrate <b>high non-linearity, second-order time-series prediction is performed (NMSE = 0.0281) for the first time in the optoelectronic mode</b>. Finally, an exemplary health-monitoring application is showcased by <b>monolithic reading and processing of a physiological time-series known as photoplethysmography (PPG) to identify atrial fibrillation</b>.