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arXiv 2408.03508cond-mat.mtrl-scics.LGcs.SYeess.SY
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基于物理信息机器学习引导的半导体异质结按需生长

On-Demand Growth of Semiconductor Heterostructures Guided by Physics-Informed Machine Learning

Institute of Semiconductors, Chinese Academy of Sciences(中国科学院半导体研究所);University of Chinese Academy of Sciences(中国科学院大学);HS Photonics Co., Ltd.(华芯光电有限公司);Zhejiang University(浙江大学)
Chao Shen, Yuan Li, Wenkang Zhan, Shujie Pan, Fuxin Lin, Kaiyao Xin, Hui Cong, Chi Xu, Xiaotian Cheng, Ruixiang Liu, Zhibo Ni, Chaoyuan Jin, Bo Xu, Siming Chen,… 展开作者

Chao Shen, Yuan Li, Wenkang Zhan, Shujie Pan, Fuxin Lin, Kaiyao Xin, Hui Cong, Chi Xu, Xiaotian Cheng, Ruixiang Liu, Zhibo Ni, Chaoyuan Jin, Bo Xu, Siming Chen, Zhongming Wei, Chunlai Xue, Zhanguo Wang, Chao Zhao

AI总结:

该研究提出面向分子束外延的自主驱动平台SemiEpi,整合物理信息机器学习实现半导体异质结按需生长,经InAs量子点生长实验验证可提升密度、发光强度并优化半高宽,为异质结制备提供高效稳定方案。

摘要:

按需开发定制化半导体异质结是满足电子与光电器件性能需求不断攀升的关键能力。然而,传统制备方法仍受限于基于仿真的设计与迭代试错优化。本文提出SemiEpi,一种面向分子束外延(MBE)的自主驱动平台,可通过原位监测与实时反馈控制执行多步半导体异质结生长。通过整合标准MBE反应炉、物理信息机器学习(ML)模型与参数初始化模块,SemiEpi能够确定最优初始条件并设计异质结生长实验,无需研究人员具备深厚的MBE工艺专业知识。作为概念验证,我们针对目标发射波长1240 nm的高密度InAs量子点(QD)生长开展了优化实验,展现了SemiEpi的效能。借助原位反射高能电子衍射监测与反馈控制调节生长温度,我们实现了5×10^10 cm^-2的量子点密度,光致发光(PL)强度提升1.6倍,半高全宽(FWHM)降至29.13 meV。综上,研究结果凸显了机器学习引导系统应对多步异质结生长挑战的潜力,可推动独立于硬件的框架开发,即便在不具备详尽生长参数知识的情况下,也能提升工艺的可重复性与稳定性。

英文摘要:

Developing tailored semiconductor heterostructures on demand represents a critical capability for addressing the escalating performance demands in electronic and optoelectronic devices. However, traditional fabrication methods remain constrained by simulation-based design and iterative trial-and-error optimization. Here, we introduce SemiEpi, a self-driving platform designed for molecular beam epitaxy (MBE) to perform multi-step semiconductor heterostructure growth through in-situ monitoring and on-the-fly feedback control. By integrating standard MBE reactors, physics-informed machine learning (ML) models, and parameter initialization, SemiEpi identifies optimal initial conditions and proposes experiments for heterostructure growth, eliminating the need for extensive expertise in MBE processes. As a proof of concept, we demonstrate the optimization of high-density InAs quantum dot (QD) growth with a target emission wavelength of 1240 nm, showcasing the power of SemiEpi. We achieve a QD density of 5 x 10^10 cm^-2, a 1.6-fold increase in photoluminescence (PL) intensity, and a reduced full width at half maximum (FWHM) of 29.13 meV, leveraging in-situ reflective high-energy electron diffraction monitoring with feedback control for adjusting growth temperatures. Taken together, our results highlight the potential of ML-guided systems to address challenges in multi-step heterostructure growth, facilitate the development of a hardware-independent framework, and enhance process repeatability and stability, even without exhaustive knowledge of growth parameters.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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