The semiconductor industry has prioritized automating repetitive tasks by closed-loop, autonomous experimentation which enables accelerated optimization of complex multi-step processes. The emergence of machine learning (ML) has ushered in automated process with minimal human intervention. In this work, we develop SemiEpi, a self-driving automation platform capable of executing molecular beam epitaxy (MBE) growth with multi-steps, continuous in-situ monitoring, and on-the-fly feedback control. By integrating standard hardware, homemade software, curve fitting, and multiple ML models, SemiEpi operates autonomously, eliminating the need for extensive expertise in MBE processes to achieve optimal outcomes. The platform actively learns from previous experimental results, identifying favorable conditions and proposing new experiments to achieve the desired results. We standardize and optimize growth for InAs/GaAs quantum dots (QDs) heterostructures to showcase the power of ML-guided multi-step growth. A temperature calibration was implemented to get the initial growth condition, and fine control of the process was executed using ML. Leveraging RHEED movies acquired during the growth, SemiEpi successfully identified and optimized a novel route for multi-step heterostructure growth. This work demonstrates the capabilities of closed-loop, ML-guided systems in addressing challenges in multi-step growth for any device. Our method is critical to achieve repeatable materials growth using commercially scalable tools. Our strategy facilitates the development of a hardware-independent process and enhancing process repeatability and stability, even without exhaustive knowledge of growth parameters.

翻译：半导体工业已优先采用闭环自主实验实现重复性任务的自动化，从而加速复杂多步工艺的优化。机器学习的出现开启了人类干预极少的自动化进程。本研究开发了SemiEpi——一个能够执行分子束外延生长的自驱动自动化平台，该平台具备多步骤执行、连续原位监测和实时反馈控制能力。通过集成标准硬件、自主开发软件、曲线拟合及多种机器学习模型，SemiEpi实现了全自主运行，无需依赖深厚的MBE工艺专业知识即可获得最优结果。该平台能主动从历史实验结果中学习，识别有利条件并提出新实验方案以实现预期目标。我们以InAs/GaAs量子点异质结构的标准化与优化生长为例，展示了机器学习引导多步生长的强大能力。通过实施温度校准获取初始生长条件，并运用机器学习对工艺进行精细调控。借助生长过程中获取的RHEED影像，SemiEpi成功识别并优化了多步异质结构生长的新路径。本工作证明了闭环机器学习引导系统在解决各类器件多步生长挑战方面的能力。该方法对实现商用可扩展工具的材料可重复生长至关重要。我们的策略有助于开发硬件无关的工艺，即使在缺乏完整生长参数知识的情况下，也能提升工艺可重复性与稳定性。