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忆阻器作为新一代非易失性存储存储器，可应用于神经形态计算等新型计算范式。TiO2材料因其高介电常数与热稳定性被广泛应用于忆阻器的功能层，然而TiO2基忆阻器仍存在稳定性较差及模拟性能欠优等瓶颈。本文引入非晶态的二维材料MoS2（（a-MoS2）与非晶态TiO2（（a-TiO2）构建了异质结结构，获得了超200次循环、高数据保持时间（>104 s）的模拟忆阻器。此外，W/a-MoS2/a-TiO2/Pt器件可通过控制扫描电压实现多级电导调制功能，通过导电机制拟合分析构建了一种基于局部纳米点诱导的导电细丝形成及断裂的物理开关模型，分析了器件实现多级电导调制的物理机制。最后，本文在W/a-MoS2/a-TiO2/Pt器件上实现了LTP、LTD功能。本文设计构建了W/a-MoS2/a-TiO2/Pt异质结结构，为过渡金属氧化物基忆阻器性能改进与应用提供了有效方案。As the field of artificial intelligence continues to evolve, it generates an escalating need for intensive computational resources and novel computing architectures. As a new generation of non-volatile memory, memristors can simulate biological synapses. This makes them ideal for neuromorphic computing, enabling brain-like learning and reasoning to significantly enhance computational capabilities. Current research on memristor dielectric materials primarily focuses on transition of metal oxides, perovskites, and organic polymers. Among these, the transition metal oxide TiO2 is widely used for the switching layer due to its high dielectric constant and excellent thermal stability. However, TiO2-based memristors face challenges including poor stability and inadequate analog performance, which limit their application in neuromorphic computing. This study developed a high-performance analog memristor using an aMoS2/a-TiO2 (amorphous MoS2/ amorphous TiO2) heterostructure, achieving over 200 stable cycles and a long data retention time exceeding 104 seconds. This device demonstrates a lower threshold voltage, higher endurance, and superior data retention, as compared to previously reported TiO2-based heterostructure memristors. Furthermore, various voltage sweep schemes were designed to successfully implement multi-level conductance modulation in the W/a-MoS2/a-TiO2/Pt device. The resistive switching mechanism of the W/a-MoS2/a-TiO2/Pt device was elucidated by combining conductive mechanism fitting with a physical model that attributes the switching to the localized formation and rupture of conductive filaments. Finally, synaptic functions like LTP and LTD were implemented in the device using square-wave pulses. A convolutional neural network leveraging these functions achieved a 95.8% accuracy in handwritten digit recognition. This study developed a W/a-MoS2/a-TiO2/Pt heterostructure that significantly enhances analog memristive performance, providing an effective strategy for improving transition metal oxide-based memristors.
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Keywords:
- Memristor /
- Heterojunction structure /
- a-MoS2 /
- Neuromorphic computing
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