Influence of Lithium Doping on Volcanic-like Perovskite Memristors and Artificial Synaptic Simulation for Neurocomputing

Abstract

Perovskite-based memristors have attracted much attention in synaptic simulation due to their outstanding electrical properties and promising potential in neuromorphic computing (NC). In this work, inorganic lead-free perovskite-based memristors composed of Ag/Cs 3 Bi 2−x Li x I 9−2x (CBL x I)/ITO (x = 0, 0.2, 0.4, 0.6) are fabricated, and the electrical properties, such as endurance, on/off ratio, and retention time, are determined. It is found that the device with x = 0.4 shows good characteristics, such as a set voltage of −0.1 V and a retention time of 10 4 s. The multilevel storage performance is investigated, and multiple synaptic characteristics, such as paired-pulse facilitation (PPF), spike-voltage-dependent plasticity (SVDP), spike-width-dependent plasticity (SWDP), spike-timing-dependent plasticity (STDP), and learning−forgetting, are simulated. The conductive mechanism of the device is analyzed and discussed with an analogy to natural volcanic rocks, which also have a large surface area, high adsorption, and high chemical inertness. An artificial neural network (ANN) based on the potentiation/depression characteristics is designed and analyzed theoretically, and a pattern recognition rate of 94.25% is accomplished. The strategy and results described in this paper provide insights into the development of nonvolatile memory devices boding well for the adoption of neuromorphic computing for image recognition.

Perovskite material-based memristors for applications in information processing and artificial intelligence

“…At the same time, Gao et al prepared a perovskite memristor device with the structure of Ag/Cs 3 Bi 2− x Li x I 9−2 x (CBL x I)/ITO ( x = 0, 0.2, 0.4, 0.6) based on inorganic lead-free perovskite materials, which was also used to construct ANNa while measuring its memristor performance. 120 The recognition accuracy of this ANN could be determined by the degree of simulation of the learned numbers in MNIST. Fig.…”

“…In 2022, Chen et al demonstrated the light response of a device to the full visible spectrum based on a ternary cation halide Cs 0.05 FA 0.81 MA 0.14 P(I 0.85 Br 0.15 ) 3 (CsFAMA) perovskite sensor computing device. 89 By observing experimental 120 Copyright 2023, American Chemical Society. (j) Schematic illustration of the constructed convolutional neural network.…”

“…(h) and (i) Recognition accuracy of the network in the flat state as a function of the training epochs and noise pixel. Panels (e)-(i) reproduced with permission 120. Copyright 2023, American Chemical Society.…”

Perovskite-Doped Modulated Color-Selective Photosynaptic Transistors for Target Object Recognition

“…Optical synaptic transistors, as one type of visual neural device, can simulate the perception of light signals in the human eye while memorizing and computing the perceived information. − In recent years, perovskite has been employed as the optoelectronic material in various fields, including solar cells, light-emitting diodes (LEDs), photodetectors, lasers, memristors, artificial synapse devices, pressure-induced emission, and so on. − The band gap of perovskite nanocrystals (PNCs) can usually be tuned by doping, which also stabilizes the lattice. − For example, Huang et al selected Zr-doped CsPbI 3 perovskite nanocrystals as the light-absorbing layer of the phototransistor memory to achieve broadband photoresponsive capability . Choi et al reported that the Mn-doped organic–inorganic halide perovskites enhanced the charge separation of the photoexcited charges and the phototransistor could emulate the synaptic functions .…”