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

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