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 density, which is fully automated and intelligent. We developed a machine learning (ML) model named 3D ResNet 50 trained using reflection high-energy electron diffraction (RHEED) videos as input 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, 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 optoelectronic and microelectronic industries.

翻译：自组装InAs/GaAs量子点在开发诸如量子点激光器和单光子源等光电器件方面具有重要价值。这些应用高度依赖于量子点的密度和质量，从而促使研究者对生长过程控制展开研究，以实现高质量外延片和器件的制备。在分子束外延中，为特定密度的量子点建立工艺参数是一项多维优化挑战，通常通过耗时且重复的试错法来解决。本文报道了一种实现任意密度量子点生长的实时反馈控制方法，该方法完全自动化且智能化。我们开发了一个名为3D ResNet 50的机器学习模型，该模型以反射高能电子衍射视频而非静态图像作为输入训练，并为工艺控制提供表面形貌的实时反馈。研究结果表明，通过成功地在近实时状态下将量子点密度从1.5E10 cm⁻²调低至3.8E8 cm⁻²或调高至1.4E11 cm⁻²，机器学习能够根据前期生长过程预测量子点的生长后密度。与传统方法相比，本方法具备原位调控能力和出色的可靠性，能显著加速材料优化过程并提高分子束外延的可重复性，代表了薄膜生长技术的重大进展。本研究证明可行的概念和方法有望应用于多种材料生长过程，将为光电子和微电子工业的半导体制造带来革命性变革。