神经形态工程学
记忆电阻器
钙钛矿(结构)
材料科学
光电子学
电极
图层(电子)
纳米技术
能量收集
非易失性存储器
电压
磁滞
MNIST数据库
能量(信号处理)
活动层
电阻随机存取存储器
储能
电子工程
逻辑门
计算机科学
频道(广播)
卤化物
晶体管
工作(物理)
切换时间
作者
Michalis Loizos,Konstantinos Rogdakis,Konstantinos Chatzimanolis,Katerina Anagnostou,Emmanuel Kymakis
标识
DOI:10.1002/aelm.202500713
摘要
ABSTRACT Memristive devices based on halide perovskites hold strong promise to provide energy‐efficient systems for the Internet of Things (IoT); however, lead (Pb) element should be minimized or ideally replaced. Herein, we introduce a multifunctional device based on AgBiI 4 Pb‐free perovskite as the active layer in a normal n‐i‐p solar cell configuration that exhibitss multiple mode‐dependent neuromorphic functions. Specifically, through electrode engineering, we show a selection between volatile or non‐volatile memristive switching when the Au top electrode is replaced by Ag. The volatile device having an Au top electrode can be used for emulating threshold‐dependent artificial neuron firing processes, while the non‐volatile device with Ag pads can emulate numerous synaptic protocols (LTP/LTD, PPF, STDP, SRDP, LTM, STM). Both volatile (V TH = −0.86 V) and non‐volatile modes possess switching low voltages <1 V, while the ON/OFF ratio of the non‐volatile system is 10 4 with a cycling endurance of 10 3 cycles and state retention of 10 3 s. Further investigation reveals that electrode type affects the conduction mechanism, as ion charge trapping and detrapping govern threshold switching with activation energy E A ≈ 0.7 eV, while the formation/rupture of Ag filaments is responsible for non‐volatile switching. The response in both modes of devices can be tuned by light, while associative learning by emulating Pavlovian learning is demonstrated. Finally, experimental non‐volatile data were used for MNIST and Fashion‐MNIST classification, achieving 97.11% and 87.56% accuracy, respectively. Our work provides insights into electrode‐engineered mode control in lead‐free perovskite systems capable for a concurrent energy harvesting (PCE ∼1%) toward self‐powered systems for neuromorphic edge computing.
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