DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.014
Publication date: 9th January 2023
Novel computing systems are currently being explored to overcome the memory bottleneck of computational systems based on the von Neumann architecture. In this context, brain-inspired paradigms implemented with memristive devices are promising candidates demonstrating low energy consumption, high scalability, large parallelism, and intrinsic high error tolerance. However, these systems are mainly limited to the emulation of relatively simple biological primitives due to the first-order dynamics of the memristive element. Typical solutions to implement higher-order neuromorphic functions require increasingly complex circuits that simulate the brain functioning, thus requiring additional computation outside the memristor device and losing the advantages in terms of area and complexity.
Here we present second-order dynamics in halide perovskite memristors which are intrinsically enabled by mixed ionic-electronic conduction. The second-order dynamics arises from the interplay between ion migration, back-diffusion, and modulable Schottky barriers. Second-order memistor dynamics allows for both rate and timing-dependent plasticity without the need for overlapping pulses, thus paving the way for a general methodology to develop neuromorphic memristive circuits with high spiking parallelism and low power consumption. By using a triplet-spike-timing-dependent-plasticity scheme, we demonstrate the implementation of the Bienenstock-Cooper-Munro learning rule. By taking inspiration from the visual processing in cortical neurons network, we implement unsupervised orientation-selective learning for binocular inputs in halide perovskite memdiodes network. these results support halide perovskite as key enabling material for neuromorphic engineering able to mimic the bioneurological phenomena of the brain by device physics.
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