High-performance one-dimensional halide perovskite crossbar memristors and synapses for neuromorphic computing

• Main Authors: Vishwanath, Sujaya Kumar, Febriansyah, Benny, Ng, Si En, Das, Tisita, Acharya, Jyotibdha, John, Rohit Abraham, Sharma, Divyam, Dananjaya, Putu Andhita, Jagadeeswararao, Metikoti, Tiwari, Naveen, Kulkarni, Mohit Ramesh Chandra, Lew, Wen Siang, Chakraborty, Sudip, Basu, Arindam, Mathews, Nripan
• Other Authors: School of Materials Science and Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Halide perovskites; Memristive behavior
• Online Access: https://hdl.handle.net/10356/178261
• Institution: Nanyang Technological University

Despite impressive demonstrations of memristive behavior with halide perovskites, no clear pathway for material and device design exists for their applications in neuromorphic computing. Present approaches are limited to single element structures, fall behind in terms of switching reliability and scalability, and fail to map out the analog programming window of such devices. Here, we systematically design and evaluate robust pyridinium-templated one-dimensional halide perovskites as crossbar memristive materials for artificial neural networks. We compare two halide perovskite 1D inorganic lattices, namely (propyl)pyridinium and (benzyl)pyridinium lead iodide. The absence of conjugated, electron-rich substituents in PrPyr+ prevents edge-to-face type π-stacking, leading to enhanced electronic isolation of the 1D iodoplumbate chains in (PrPyr)[PbI3], and hence, superior resistive switching performance compared to (BnzPyr)[PbI3]. We report outstanding resistive switching behaviours in (PrPyr)[PbI3] on the largest flexible crossbar implementation (16 × 16) to date - on/off ratio (>105), long term retention (105 s) and high endurance (2000 cycles). Finally, we put forth a universal approach to comprehensively map the analog programming window of halide perovskite memristive devices - a critical prerequisite for weighted synaptic connections in artificial neural networks. This consequently facilitates the demonstration of accurate handwritten digit recognition from the MNIST database based on spike-timing-dependent plasticity of halide perovskite memristive synapses.