Real‐Time Self‐Optimization of Quantum Dot Laser Emissions During Machine Learning‐Assisted Epitaxy - Chao Shen; Wenkang Zhan; Shujie Pan; Hongyue Hao; Ning Zhuo; Kaiyao Xin; Hui Cong; Chi Xu; Bo Xu; Tien Khee Ng; Siming Chen; Chunlai Xue; et al. (Advanced Science)
Abstract 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, in situ reflection high‐energy electron diffraction (RHEED) is integrated 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. InAs QDs on GaAs substrates are successfully optimized, with a 3.2‐fold increase in PL intensity and a reduction in full width at half maximum (FWHM) from 36.69 to 28.17 meV. 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 cm− 2 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.