Publication date: 21st July 2025
Metal halide perovskites are mixed ionic-electronic conductors. This mixed conduction allows them to be used for memristive applications. We have developed a cross-bar back-contacted device architecture for which we have demonstrated both artificial synapses and neurons with very low energy consumption.
However, perovskites are also excellent light absorbers. We show that when light is used to switch the state of the memristor, either in combination with voltage, or in addition to it, it can also be used to alter the properties of the memristor.
By using light as an input, perovskite memristors are ideally suited to perform in-sensor computation on visual input. We simulate such an application using the measurement parameters as input. We map the MNIST and N-MNIST datasets based on 4-bit inputs and train linear readout layers for classification. In this configuration, we find classification accuracies of up to 92.33 ± 0.06% and 84.34 ± 0.03%for MNIST and N-MNIST, respectively, with only minor deterioration by measurement noise. This result is more than 10% higher compared to a linear classifier for the N-MNIST dataset. The microscale device architecture lends itself well to high-density sensor arrays, ideally suited for efficient in-sensor computing.
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