标题： 机器学习辅助外延量子点激光器发射的原位自优化 显示英文标题

标题： In-situ Self-optimization of Quantum Dot Emission for Lasers by Machine-Learning Assisted Epitaxy

摘要： 传统优化光源发射的方法依赖于耗时的试错方法。尽管在生长过程中对光源增益介质发射进行原位优化是最理想的，但这一目标尚未实现。在这项工作中，我们将原位反射高能电子衍射（RHEED）与机器学习（ML）相结合，关联了表面重构与InAs/GaAs量子点（QDs）的光致发光（PL），这些量子点作为激光器的有源区。我们采用轻量级ResNet-GLAM模型处理RHEED数据作为输入，以实现实时处理，从而有效识别光学性能。这种方法指导了生长参数的动态优化，允许实时反馈控制来调整激光器中的量子点发射。我们在GaAs衬底上的InAs量子点成功实现了优化，在初始亚最佳生长条件下，PL强度提高了3.2倍，半高全宽（FWHM）从36.69 meV减少到28.17 meV。我们的自动化、原位自优化激光器具有5层InAs量子点，在室温下实现了1240 nm的电泵浦连续波操作，阈值电流低至150 A/cm²，其性能与通过传统手动多参数优化方法生长的样品相当。这些结果标志着向智能化、低成本和可重复的发光器件生产迈出了重要一步。 显示英文摘要

摘要： Traditional methods for optimizing light source emissions rely on a time-consuming trial-and-error approach. While in-situ optimization of light source gain media emission during growth is ideal, it has yet to be realized. In this work, we integrate in-situ reflection high-energy electron diffraction (RHEED) with machine learning (ML) to correlate the surface reconstruction with the photoluminescence (PL) of InAs/GaAs quantum dots (QDs), which serve as the active region of lasers. A lightweight ResNet-GLAM model is employed for the real-time processing of RHEED data as input, enabling effective identification of optical performance. This approach guides the dynamic optimization of growth parameters, allowing real-time feedback control to adjust the QDs emission for lasers. We successfully optimized InAs QDs on GaAs substrates, with a 3.2-fold increase in PL intensity and a reduction in full width at half maximum (FWHM) from 36.69 meV to 28.17 meV under initially suboptimal growth conditions. Our automated, in-situ self-optimized lasers with 5-layer InAs QDs achieved electrically pumped continuous-wave operation at 1240 nm with a low threshold current of 150 A/cm2 at room temperature, an excellent performance comparable to samples grown through traditional manual multi-parameter optimization methods. These results mark a significant step toward intelligent, low-cost, and reproductive light emitters production.