Ionotronic Halide Perovskite Drift‐Diffusive Synapses for Low‐Power Neuromorphic Computation. Issue 51 (17th October 2018)

• Record Type: Journal Article
• Authors: John, Rohit Abraham; Yantara, Natalia; Ng, Yan Fong; Narasimman, Govind; Mosconi, Edoardo; Meggiolaro, Daniele; Kulkarni, Mohit R.; Gopalakrishnan, Pradeep Kumar; Nguyen, Chien A.; De Angelis, Filippo; Mhaisalkar, Subodh G.; Basu, Arindam; Mathews, Nripan
• Is Part Of: Advanced materials. Volume 30:Issue 51(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-10-17
• Subjects: halide perovskites -- ion migration -- ionic semiconductors -- neuromorphic computing -- synaptic plasticity; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.201805454
• Languages: English
• ISSNs: 0935-9648

Abstract

Abstract: 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, ionic–electronic coupling in halide perovskite semiconductors is utilized to create memristive synapses with a dynamic continuous transition of conductance states. Coexistence 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. Abstract : Exploiting the mixed electronic–ionic conduction in halide perovskites, artificial synapses are realized to mimic short‐ and long‐term plasticity rules—the basis of learning and cognition. Coexistence of carrier‐injection barriers and ion migration in the perovskite films defines the degree of synaptic plasticity, more notable for larger organic ammonium cations than the inorganic cesium counterparts.