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Applied Physics 2026
钛酸钙基忆阻器的多态信息传输在神经形态计算中的应用
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Abstract:
集成电路的工艺技术已进入发展瓶颈期，进一步实现高计算能力集成已成为一项重大挑战。在这项工作中，基于层状钙钛矿结构的Ca3Ti2O7的神经形态器件已被开发出来，而界面缺陷状态主导的载流子弛豫为实现具有突触控制能力的离子动力学提供了一条可靠的途径。测试结果表明，该结构具有与人工突触相似的电流增益/抑制特性，并且这种稳健的突触生长可控性可用于多比特信息处理的实现，相对传统计算架构可提供超高的单位信息处理集成度的能力。神经网络中卷积图像处理的96.8%的识别准确率体现了其计算潜力，有望超越冯·诺依曼系统的框架。
The process technology of integrated circuits has entered a developmental plateau, and further advancement of high-computing power integration has become a major challenge. In this work, neuromorphic devices based on Ruddlesden-Popper (RP) structured Ca3Ti2O7 have been developed, and the interface defect state-dominated carrier relaxation provides a reliable pathway to achieve synaptically controllable ionic dynamics. Test results show that the structure has current gain/suppression characteristics similar to those of artificial synapses, and this robust synaptic growth controllability can be utilized for the realization of multi-bit information processing. Compared with traditional computing architectures, it offers the ability to achieve an extremely high unit information processing integration rate. The 96.8% recognition accuracy of convolutional image processing in neural networks exemplifies the computational potential that promises to exceed the framework of von Neumann systems.
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