In-situ Self-optimization of Quantum Dot Emission for Lasers by Machine-Learning Assisted Epitaxy
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916464402890752 |
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| author | 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 Zhao, Chao |
| author_facet | 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 Zhao, Chao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00332 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | In-situ Self-optimization of Quantum Dot Emission for Lasers by Machine-Learning Assisted Epitaxy 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 Zhao, Chao Mesoscale and Nanoscale Physics Machine Learning 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. |
| title | In-situ Self-optimization of Quantum Dot Emission for Lasers by Machine-Learning Assisted Epitaxy |
| topic | Mesoscale and Nanoscale Physics Machine Learning |
| url | https://arxiv.org/abs/2411.00332 |