Proceedings of International Conference on Hybrid and Organic Photovoltaics (HOPV23)
DOI: https://doi.org/10.29363/nanoge.hopv.2023.189
Publication date: 30th March 2023
Recent advancements in computational systems have shown that it is possible to respond instantly to environmental stimuli with minimal energy consumption, making it necessary to develop miniature elements that emulate natural cognitive processes. One key area of focus is the brain, which provides a model for information transmission, learning, computation, and signal processing. Synapses and neurons, in particular, rely on ionic currents and electrochemical potentials to facilitate these processes. Halide perovskite is a material that has garnered attention for its optoelectronic properties, as well as its switchable behavior that mimics biophysical systems. In this study, we explore how the unique properties of halide perovskite can be harnessed for effective neuromorphic elements that rely on the physical properties of the device. We use impedance spectroscopy and equivalent circuits to characterize highly nonlinear models and behaviors that elicit neuron-style behavior. By controlling the exaggerated hysteresis in memristor devices, we can obtain domain characteristics such as spiking and potentiation that are essential for establishing edge computing in systems that operate with minimal power consumption.1-3
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(2) Bisquert, J.; Guerrero, A. Chemical Inductor, J. Am. Chem. Soc. 2022, 144, 5996–6009.
(3) Bisquert, J. Negative inductor effects in nonlinear two-dimensional systems. Oscillatory neurons and memristors, Chemical Physics Reviews 2022, 3, 041305.