1. In-situ Self-optimization of Quantum Dot Emission for Lasers by Machine-Learning Assisted Epitaxy
- Author
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Shen, Chao, Zhan, Wenkang, Pan, Shujie, Hao, Hongyue, Zhuo, Ning, Xin, Kaiyao, Cong, Hui, Xu, Chi, Xu, Bo, Ng, Tien Khee, Chen, Siming, Xue, Chunlai, Liu, Fengqi, Wang, Zhanguo, and Zhao, Chao
- Subjects
Condensed Matter - Mesoscale and Nanoscale Physics ,Computer Science - Machine Learning - 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, 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., Comment: 5 figures
- Published
- 2024