Complex environments generate a large amount of information. This is typically achieved by employing a progressively larger number of sensor nodes and power-draining central processing units to gather and process the temporal input. However, this is inefficient and unscalable in edge computing devices. Unifying the sensing, memory, and computing units is the holy grail of the Internet of Things (IoT). Optimization of edge computing devices for real-time input calls for circumventing the latency arising from data transfer between the components. Unconventional computers, akin to a biological brain, utilise the intrinsic dynamics of physical features in a material substrate to implement systems without a clear distinction between the computational, memory, and sensing units. Therefore, there is a need to innovate in materials to aid or potentially replace the information processing archetype.            

Halide perovskites are a class of materials potentially viable for this application. Anomolous effects such as light soaking, JV hysteresis, and slow open circuit voltage decay (OCVD) are the phenomena which establish the theme for their integration in neuromorphic computing.

In this work, the transient OCVD has been probed in a formamidinium lead bromide (FAPbBr₃)-based solar cell architecture. Upon stimulation through light pulses, the device shows characteristics of short-term, fading memory with a decay time constant of ~0.3 seconds. Additionally, a non-linear dependence of the paired-pulse facilitation on the number and timing of pulses is observed. The dynamics check all the prerequisites for application as a single-node physical reservoir computing system. Subsequently, slices of spatiotemporal information from a handwritten digit image can be fed into the physical reservoir as sensory inputs. A neural network can then be trained and tested with virtual nodes derived from the compression of these inputs to recognize handwritten digits. Interestingly, due to our device's high distinguishability of a 4-bit slice (with a gain of 200%), the neural network performed on par (at 95% testing accuracy) compared to the baseline. Encouraged by the results, we also simulated and observed a performance trade-off between the testing and validation performances at higher compression.

To process temporal input, physical reservoir computers have been realized in several electrical, magnetic, mechanical, photonic, and optoelectronic systems. However, we introduce a photovoltaic reservoir computing network with photovoltage as the read-out variable. This gives the system an edge over other implementations as voltage output can be directly fed to TFTs, comparators, or ADCs to manifest an entirely physical neural network.