Robust Lead‐Free Perovskite Nanowire Array‐Based Artificial Synapses Exemplifying Gestalt Principle of Closure via a Letter Recognition Scheme

Abstract

The Gestalt principles of perceptual learning elucidate how the human brain categorizes and comprehends a set of visual elements grouped together. One of the principles of Gestalt perceptual learning is the law of closure which propounds that human perception has the proclivity to visualize a fragmented object as a preknown whole by bridging the missing gaps. Herein, a letter recognition scheme emulating the Gestalt closure principle is demonstrated, utilizing artificial synapses made of 3D integrated MA3Bi2I9 (MBI) perovskite nanowire (NW) array. The artificial synapses exhibit short‐term plasticity (STP) and long‐term potentiation (LTP) and a transition from STP to LTP with increasing number of input electrical pulses. Initiatory ab initio molecular dynamics (AIMD) simulations attribute the conductance change in the MBI NW artificial synapses to the rotation of MA+ clusters, culminating in charge exchange between MA+ and Bi2I93−. Each device yields 40 conductance states with excellent retention >105 s, minimal variation (2σ/mean) <10%, and endurance of ≈105 cycles. MBI NW‐based artificial neural network (ANN) is constructed to recognize fragmented letters alike their distinction in unabridged form and also the gradual withering of synaptic connectivity with engendered missing fragments is demonstrated, thereby successfully implementing Gestalt closure principle.

Keywords

• artificial synapses
• Gestalt principle of closure
• letter recognition
• perovskite nanowires