Publication date: 21st July 2025
Biological neurons routinely execute higher-order computations that artificial neural networks usually require multiple layers to achieve. Replicating this versatility in hardware demands circuits that extend beyond simple relaxation oscillators to include (i) self-resonant, inductive elements and (ii) ultrasmooth negative-differential-resistance (NDR) devices. Here we leverage ion-mediated recombination in metal-halide perovskite diodes which introduces a phase-lag between voltage and current. This enables the diode with an intrinsic electrochemical inductance. For the first time, we quantify a quality factor of Q ~ 3 and observe both a fundamental resonance and its harmonics in the perovskite diode. Unlike conventional abrupt switching NDR, a continuous low-hysteresis NDR can be achieved by using an electrostatically gated silicon thyristor. The resulting perovskite–thyristor combination supports rich nonlinear dynamics, including saddle-node and Hopf bifurcations, thereby enabling both integrator- and resonator-type artificial neurons. Integrator neurons encode stimulus onset and termination with high temporal precision, whereas resonator neurons exhibit sharp frequency selectivity and native XOR (anti-coincidence) detection. These findings introduce a framework for designing versatile neuromorphic systems with unique hysteretic properties from memristive materials, offering a practical route to compact neurons that match the computational richness of their biological counterparts.
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