基于硫基相变材料的存内计算器件与集成芯片(特邀)  

In-Memory Computing Devices and Integrated Chips Based on Chalcogenide Phase Change Materials(Invited)

作　　者：许恺 贠亦婷 张嘉欣 李想[1] 王威权 魏茂良 雷坤皓 李钧颖 林宏焘 Xu Kai;Yun Yiting;Zhang Jiaxin;Li Xiang;Wang Weiquan;Wei Maoliang;Lei Kunhao;Li Junying;Lin Hongtao(College of Information Science and Electronic Engineering,The State Key Lab of Brain-Machine Intelligence,Key Laboratory of Micro-Nano Electronics and Smart System of Zhejiang Province,Zhejiang University,Hangzhou 310027,Zhejiang,China;Hangzhou Institute for Advanced Study,University of Chinese Academy of Sciences,Hangzhou 310024,Zhejiang,China)

机构地区：[1]浙江大学信息与电子工程学院,脑机智能国家重点实验室,微纳电子学与智能系统浙江省重点实验室,浙江杭州310027 [2]国科大杭州高等研究院,浙江杭州310024

出　　处：《光学学报》2024年第15期382-401,共20页Acta Optica Sinica

基　　金：国家重点研发计划青年科学家项目(2021YFB2801300);国家重点研发计划重点专项(2023YFB2806504);国家自然科学基金重大研究计划集成项目(92150302);国家自然科学基金青年项目(62105287);西湖光电研究院重点项目(2024GD002);中国科学院上海技术物理研究所红外物理国家重点实验室开放基金。

摘　　要：从材料、集成工艺、器件和网络4个方面回顾了硫基相变材料及其集成器件与集成存内计算光网络的研究进展,聚焦于材料损耗、器件性能、大规模集成工艺和网络可扩展性问题,探讨了存内计算器件与集成芯片存在的挑战和可行的解决方案。构建高速、低功耗的硫基相变材料通用型存内计算芯片,是实现大数据量计算应用场景下高性能加速、计算能效大幅提升的一个极具前景的技术路径。Significance With the emergence of artificial intelligence,there has been a significant surge in demand for hardware performance.Stronger computing power has been achieved over the past 40 years by scaling down transistors to gain higher computing density and improving memory bandwidth to overcome communication latency.However,pushing the limits in the density and complexity of integrated circuits(ICs)has caused the traditional von Neumann computing architecture to become inadequate in supporting fields like automatic driving,big data,and the Internet of Things(IoTs).“In-memory computing”mimics the human brain’s thinking process,integrating computing functions into memory to avoid data exchange bottlenecks and transmission time decay in conventional computers.This paradigm is promising due to its low latency,parallelism,and scalability.Currently,various technological forms for in-memory computing have been proposed.Photonic in-memory computing hardware based on chalcogenide phase change materials(PCMs)combines existing dielectric materials widely used in memory technology with novel optical computing technology.Benefitting from anti-electromagnetic interference,parallelism from light’s multiphysical dimensions,zero static power consumption,high thermal crosstalk thresholds,and reduced computing time,these chips have the potential to accelerate and improve energy efficiency in data-intensive scenarios.Progress Our paper reviews research progress on chalcogenide PCMs,integrated devices,and optical networks for in-memory computing applications.Furthermore,we discuss challenges and future developments regarding in-memory computing devices and integrated chips.In terms of materials,chalcogenide PCMs attract attention due to their nonvolatility and optical property contrast.We analyze the principles of optimizing chalcogenide PCMs’performance along with historical development and strategies for material improvement.Besides,we introduce the development history of application-oriented chalcogenide PCMs with low

关 键 词：集成光学器件 存内计算 硅基光子学 硫基光子学 相变材料 光学神经网络 

分 类 号：TN256[电子电信—物理电子学]