Thin film deposition is an essential step in the semiconductor process. During preparation or loading, the substrate is exposed to the air unavoidably, which has motivated studies of the process control to remove the surface oxide before thin film deposition. Optimizing the deoxidation process in molecular beam epitaxy (MBE) for a random substrate is a multidimensional challenge and sometimes controversial. Due to variations in semiconductor materials and growth processes, the determination of substrate deoxidation temperature is highly dependent on the grower's expertise; the same substrate may yield inconsistent results when evaluated by different growers. Here, we employ a machine learning (ML) hybrid convolution and vision transformer (CNN-ViT) model. This model utilizes reflection high-energy electron diffraction (RHEED) video as input to determine the deoxidation status of the substrate as output, enabling automated substrate deoxidation under a controlled architecture. This also extends to the successful application of deoxidation processes on other substrates. Furthermore, we showcase the potential of models trained on data from a single MBE equipment to achieve high-accuracy deployment on other equipment. In contrast to traditional methods, our approach holds exceptional practical value. It standardizes deoxidation temperatures across various equipment and substrate materials, advancing the standardization research process in semiconductor preparation, a significant milestone in thin film growth technology. The concepts and methods demonstrated in this work are anticipated to revolutionize semiconductor manufacturing in optoelectronics and microelectronics industries by applying them to diverse material growth processes.

翻译：薄膜沉积是半导体工艺中的关键步骤。在制备或装载过程中，衬底不可避免地暴露于空气中，这促使人们开展工艺控制研究，以在薄膜沉积前去除表面氧化层。针对随机衬底优化分子束外延（MBE）中的脱氧过程是一项多维挑战，时常存在争议。由于半导体材料及生长过程的差异，衬底脱氧温度的确定高度依赖于操作者的经验；同一衬底由不同操作者评估时可能得出不一致的结果。本研究采用机器学习混合卷积与视觉变换器（CNN-ViT）模型。该模型以反射高能电子衍射（RHEED）视频为输入，输出衬底脱氧状态，从而在受控架构下实现自动化衬底脱氧。该方法还可扩展应用于其他衬底的脱氧过程。此外，我们展示了基于单台MBE设备数据训练的模型在其他设备上实现高精度部署的潜力。与传统方法相比，本方法具有卓越的实用价值。它统一了不同设备和衬底材料的脱氧温度标准，推动了半导体制备中的标准化研究进程，这是薄膜生长技术的重要里程碑。本工作展示的概念与方法有望通过应用于多样化材料生长过程，革新光电与微电子产业中的半导体制造工艺。