Lead-Free Cs₃Bi₂I₉ Perovskite Memristors for Energy-Efficient Neuromorphic Computing

Abstract

In-memory computing offers a transformative alternative to traditional von Neumann architecture, with memristors enabling accelerated, low-power computation. Halide perovskites, known for ion migration with low activation energy and synapse-like switching behavior, hold great potential but face challenges in conductance linearity and predictability. Here, we report flexible lead-free Cs 3 Bi 2 I 9 8 × 8 crossbar memristors exhibiting bipolar resistive switching with a high on/off ratio (10 6 ), endurance (10 4 cycles), long retention (10 5 s), and a device yield exceeding 93%. Electrical pulse engineering reveals synaptic behaviors such as paired-pulse facilitation, potentiation, and depression with excellent linearity and minimal variability. In situ training of artificial neural networks, including MLP and VGG-8, achieves 88.19% accuracy on reduced MNIST and 91.38% on CIFAR-10 data sets. This work demonstrates energy-efficient, high-performance neuromorphic hardware, paving the way for advanced parallel computing to address the growing demands of AI and data science.