Self-assembled InAs/GaAs quantum dots (QDs) have properties highly valuable for developing various optoelectronic devices such as QD lasers and single photon sources. The applications strongly rely on the density and quality of these dots, which has motivated studies of the growth process control to realize high-quality epi-wafers and devices. Establishing the process parameters in molecular beam epitaxy (MBE) for a specific density of QDs is a multidimensional optimization challenge, usually addressed through time-consuming and iterative trial-and-error. Here, we report a real-time feedback control method to realize the growth of QDs with arbitrary and precise density, which is fully automated and intelligent. We developed a machine learning (ML) model named 3D ResNet, specially designed for training RHEED videos instead of static images and providing real-time feedback on surface morphologies for process control. As a result, we demonstrated that ML from previous growth could predict the post-growth density of QDs, by successfully tuning the QD densities in near-real time from 1.5E10 cm-2 down to 3.8E8 cm-2 or up to 1.4E11 cm-2. Compared to traditional methods, our approach, with in-situ tuning capabilities and excellent reliability, can dramatically expedite the material optimization process and improve the reproducibility of MBE growth, constituting significant progress for thin film growth techniques. The concepts and methodologies proved feasible in this work are promising to be applied to a variety of material growth processes, which will revolutionize semiconductor manufacturing for microelectronic and optoelectronic industries.

翻译：自组装InAs/GaAs量子点因其在量子点激光器和单光子源等光电器件开发中的重要价值而备受关注。这些器件的性能强烈依赖于量子点的密度与质量，这促使研究者探索生长过程控制方法以制备高质量的外延晶片和器件。在分子束外延（MBE）中，为实现特定密度量子点而确立工艺参数是一项多维优化挑战，通常需通过耗时且迭代的试错法解决。本文报道了一种全自动智能化的实时反馈控制方法，可实现在任意精确密度下生长量子点。我们开发了名为3D ResNet的机器学习模型，该模型专为训练RHEED视频（而非静态图像）设计，可实时提供表面形貌反馈以控制工艺过程。实验证明，利用前期生长数据的机器学习可预测量子点生长后密度，成功将近实时调控的量子点密度从1.5×10^10 cm⁻²降至3.8×10^8 cm⁻²或升至1.4×10^11 cm⁻²。与传统方法相比，本方法凭借原位调控能力与卓越可靠性，可显著加速材料优化进程并提升MBE生长的可重复性，标志着薄膜生长技术的重大突破。本研究验证的概念与方法有望推广至多种材料生长工艺，将革新微电子与光电子产业的半导体制造技术。