Materials science

Electronic engineering

Optoelectronics

Engineering

Artificial intelligence

Ambipolar diffusion

Bioelectronics

Physics

Neuromorphic engineering

Electrode

Nanotechnology

Perovskite (structure)

Quantum mechanics

Memristor

Computer science

Plasma

Biosensor

Chemical engineering

Artificial neural network

Advanced Materials

With the current research impetus on neuromorphic computing hardware, realizing efficient drift and diffusive memristors are considered critical milestones for the implementation of readout layers, selectors, and frameworks in deep learning and reservoir computing networks. Current demonstrations are predominantly limited to oxide insulators with a soft breakdown behavior. While organic ionotronic electrochemical materials offer an attractive alternative, their implementations thus far have been limited to features exploiting ionic drift a.k.a. drift memristor technology. Development of diffusive memristors with organic electrochemical materials is still at an early stage, and modulation of their switching dynamics remains unexplored. Here, halide perovskite (HP) memristive barristors (diodes with variable Schottky barriers) portraying tunable diffusive dynamics and ionic drift are proposed and experimentally demonstrated. An ion permissive poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate interface that promotes diffusive kinetics and an ion source nickel oxide (NiO x ) interface that supports drift kinetics are identified to design diffusive and drift memristors, respectively, with methylammonuim lead bromide (CH 3 NH 3 PbBr 3 ) as the switching matrix. In line with the recent interest on developing artificial afferent nerves as information channels bridging sensors and artificial neural networks, these HP memristive barristors are fashioned as nociceptive and synaptic emulators for neuromorphic sensory signal computing.

Diffusive and Drift Halide Perovskite Memristive Barristors as Nociceptive and Synaptic Emulators for Neuromorphic Computing