Rohit John1 Natalia Yantara2 1 Filippo De Angelis3 4 Subodh Mhaisalkar2 1 Arindam Basu1 Nripan Mathews1 2

1, Nanyang Technological University, Singapore, , Singapore
2, Energy Research Institute @ NTU (ERI@N), Nanyang Technological University, Singapore, , Singapore
3, Computational Laboratory for Hybrid/Organic Photovoltaics (CLHYO), CNR-ISTM, Perugia, , Italy
4, D3-Computation, Istituto Italiano di Tecnologia, Genova, , Italy

Emulation of brain-like signal processing is the foundation for development of efficient learning circuitry, but few devices offer the tunable conductance range necessary for mimicking spatiotemporal plasticity in biological synapses. An ionic semiconductor which couples electronic transitions with drift-diffusive ionic kinetics would enable energy-efficient analog-like switching of metastable conductance-states. Here, we utilize ionic-electronic coupling in halide perovskite semiconductors to create memristive synapses with a dynamic continuous transition of conductance-states. Co-existence of carrier injection barriers and ion migration in the perovskite films defines the degree of synaptic plasticity, more notable for the larger organic ammonium and formamidinium cations than the inorganic cesium counterpart. Optimized pulsing schemes facilitates a balanced interplay of short and long-term plasticity rules like paired-pulse facilitation and spike-time dependent plasticity, cardinal for learning and computing. Trained as a memory array, halide perovskite synapses demonstrate reconfigurability, learning, forgetting and fault tolerance analogous to the human brain. Network-level simulations of unsupervised learning of handwritten digit images utilizing experimentally derived device parameters, validates the utility of these memristors for energy-efficient neuromorphic computation, paving way for novel ionotronic neuromorphic architectures with halide perovskites as the active material.