Li Mengdie, He Yanyan, Wang Chengyang, Io Weng Fu, Guo Feng, Jie Wenjing, Hao Jianhua
College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China.
Department of Applied Physics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, 999077, China.
Small. 2025 Jun 22:e2412314. doi: 10.1002/smll.202412314.
It is essential to explore the interactions between intrinsic ferroelectricity and ionic activities in 2D ferroelectrics for theoretically understanding and experimentally modulating device performance. Due to the tendency of Cu migration in ferroelectric copper indium thiophosphate (CIPS) and formation of Cu conductive filaments, herein, Cu-deficient CIPS (CIPS) is employed to investigate resistive switching (RS). Different from CIPS with controllable threshold switching and write-once read-many-times (WORM) behaviors, CIPS shows stable non-volatile digital and analog RS behaviors by controlling the operation voltage. Owing to the formation of non-stoichiometric InPS (IPS) with metallic phase at the low-resistance state, the fabricated memristors demonstrate high ON/OFF ratio up to 5 × 10 and high endurance stability (>2000 cycles), which can be utilized to implement multilevel storage. And more intriguing, amplitude-dependent and polarity-independent long-term potentiation and depression can be simulated based on the analog memristors. Artificial neural network based on CIPS synaptic memristors can realize handwritten digit recognition with the accuracy of 91.15%. Even after considering the cycle-to-cycle and device-to-device variations of the synaptic functions, the accuracy remains as high as 90.71%. Such investigations pave the way toward highly reliable memristors base on 2D ferroelectrics with potential applications in multilevel storage and neuromorphic computing.
为了从理论上理解并通过实验调节二维铁电体中本征铁电性与离子活性之间的相互作用对器件性能的影响，这一探索至关重要。由于铁电硫代磷酸铜铟（CIPS）中铜有迁移的趋势并会形成铜导电细丝，因此，本文采用缺铜的CIPS（CIPS）来研究电阻开关（RS）。与具有可控阈值开关和一次写入多次读取（WORM）行为的CIPS不同，CIPS通过控制工作电压表现出稳定的非易失性数字和模拟RS行为。由于在低电阻状态下形成了具有金属相的非化学计量比InPS（IPS），所制备的忆阻器展现出高达5×10的高开/关比和高耐久性稳定性（>2000次循环），可用于实现多级存储。更有趣的是，可以基于模拟忆阻器模拟幅度依赖和极性无关的长期增强和抑制。基于CIPS突触忆阻器的人工神经网络能够以91.15%的准确率实现手写数字识别。即使考虑到突触功能的逐周期和器件间变化，准确率仍高达90.71%。这些研究为基于二维铁电体的高可靠性忆阻器铺平了道路，其在多级存储和神经形态计算中具有潜在应用。
