Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing

Abstract

Many in-memory computing frameworks demand electronic devices with specific switching characteristics to achieve the desired level of computational complexity. Existing memristive devices cannot be reconfigured to meet the diverse volatile and non-volatile switching requirements, and hence rely on tailored material designs specific to the targeted application, limiting their universality. “Reconfigurable memristors” that combine both ionic diffusive and drift mechanisms could address these limitations, but they remain elusive. Here we present a reconfigurable halide perovskite nanocrystal memristor that achieves on-demand switching between diffusive/volatile and drift/non-volatile modes by controllable electrochemical reactions. Judicious selection of the perovskite nanocrystals and organic capping ligands enable state-of-the-art endurance performances in both modes – volatile (2 × 106 cycles) and non-volatile (5.6 × 103 cycles). We demonstrate the relevance of such proof-of-concept perovskite devices on a benchmark reservoir network with volatile recurrent and non-volatile readout layers based on 19,900 measurements across 25 dynamically-configured devices.

On-device Synaptic Memory Consolidation using Fowler-Nordheim Quantum-tunneling

“…However, in order to meet the specifications of a targetted neural network, tedious fabrication processes of such memristors are required [35]. Reconfigurable memristors that employ halide perovskite nanocrystals [36] are more generalized in their application and show capability of prolonged usage of about 5, 600 cycles/updates. In comparison, the FN-synapse has a highly extensive scope of applications as the update size of any network it is employed in can be adjusted appropriately by shaping the input pulse as required.…”

Donor Engineering Tuning the Analog Switching Range and Operational Stability of Organic Synaptic Transistors for Neuromorphic Systems

“…[ 5 ] For simulating synaptic behavior, several types of synaptic devices have been developed, including two‐terminal synaptic devices and three‐terminal synaptic devices. [ 6–8 ] Compared with two‐terminal devices, three‐terminal devices have the advantages of simultaneously receiving and reading stimuli, thus being one of the most promising candidates for constructing the next‐generation artificial intelligence. [ 9–11 ] Organic semiconducting materials provide the perfect platform for three‐terminal synaptic devices with the merits of being facile solution processable, mechanical flexibility, large‐area printing and favorable biocompatibility.…”

Design of the Threshold-Controllable Memristor Emulator Based on NDR Characteristics

“…A memristor is a non-linear resistor with memory characteristics, and its i-v characteristic curve is related to the frequency. Its proven good performance implies significant potential in the fields of chaotic circuits [3][4][5], nonvolatile memory [6,7], digital logic [8,9], artificial neural networks [10][11][12], and non-linear circuits. In general, the research of memristors includes physical implementation [2,13,14], applications in electronic circuits [15,16], and memristor emulators [17][18][19][20][21][22][23][24][25][26].…”