Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity

• Published in: Science advances 2022-12, Vol.8 (51), p.eade0072-eade0072
• Main Authors: John, Rohit Abraham, Milozzi, Alessandro, Tsarev, Sergey, Brönnimann, Rolf, Boehme, Simon C, Wu, Erfu, Shorubalko, Ivan, Kovalenko, Maksym V, Ielmini, Daniele
• Format: Article
• Language: English
• Subjects: Engineering; Physical and Materials Sciences; SciAdv r-articles
• ISSN: 2375-2548; 2375-2548
• DOI: 10.1126/sciadv.ade0072

With increasing computing demands, serial processing in von Neumann architectures built with zeroth-order complexity digital circuits is saturating in computational capacity and power, entailing research into alternative paradigms. Brain-inspired systems built with memristors are attractive owing to their large parallelism, low energy consumption, and high error tolerance. However, most demonstrations have thus far only mimicked primitive lower-order biological complexities using devices with first-order dynamics. Memristors with higher-order complexities are predicted to solve problems that would otherwise require increasingly elaborate circuits, but no generic design rules exist. Here, we present second-order dynamics in halide perovskite memristive diodes (memdiodes) that enable Bienenstock-Cooper-Munro learning rules capturing both timing- and rate-based plasticity. A triplet spike timing-dependent plasticity scheme exploiting ion migration, back diffusion, and modulable Schottky barriers establishes general design rules for realizing higher-order memristors. This higher order enables complex binocular orientation selectivity in neural networks exploiting the intrinsic physics of the devices, without the need for complicated circuitry.