On-Demand Growth of Semiconductor Heterostructures Guided by Physics-Informed Machine Learning
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2024
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| _version_ | 1866912627654918144 |
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| author | Shen, Chao Li, Yuan Zhan, Wenkang Pan, Shujie Lin, Fuxin Xin, Kaiyao Cong, Hui Xu, Chi Cheng, Xiaotian Liu, Ruixiang Ni, Zhibo Jin, Chaoyuan Xu, Bo Chen, Siming Wei, Zhongming Xue, Chunlai Wang, Zhanguo Zhao, Chao |
| author_facet | Shen, Chao Li, Yuan Zhan, Wenkang Pan, Shujie Lin, Fuxin Xin, Kaiyao Cong, Hui Xu, Chi Cheng, Xiaotian Liu, Ruixiang Ni, Zhibo Jin, Chaoyuan Xu, Bo Chen, Siming Wei, Zhongming Xue, Chunlai Wang, Zhanguo Zhao, Chao |
| contents | Developing tailored semiconductor heterostructures on demand represents a critical capability for addressing the escalating performance demands in electronic and optoelectronic devices. However, traditional fabrication methods remain constrained by simulation-based design and iterative trial-and-error optimization. Here, we introduce SemiEpi, a self-driving platform designed for molecular beam epitaxy (MBE) to perform multi-step semiconductor heterostructure growth through in-situ monitoring and on-the-fly feedback control. By integrating standard MBE reactors, physics-informed machine learning (ML) models, and parameter initialization, SemiEpi identifies optimal initial conditions and proposes experiments for heterostructure growth, eliminating the need for extensive expertise in MBE processes. As a proof of concept, we demonstrate the optimization of high-density InAs quantum dot (QD) growth with a target emission wavelength of 1240 nm, showcasing the power of SemiEpi. We achieve a QD density of 5 x 10^10 cm^-2, a 1.6-fold increase in photoluminescence (PL) intensity, and a reduced full width at half maximum (FWHM) of 29.13 meV, leveraging in-situ reflective high-energy electron diffraction monitoring with feedback control for adjusting growth temperatures. Taken together, our results highlight the potential of ML-guided systems to address challenges in multi-step heterostructure growth, facilitate the development of a hardware-independent framework, and enhance process repeatability and stability, even without exhaustive knowledge of growth parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03508 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On-Demand Growth of Semiconductor Heterostructures Guided by Physics-Informed Machine Learning Shen, Chao Li, Yuan Zhan, Wenkang Pan, Shujie Lin, Fuxin Xin, Kaiyao Cong, Hui Xu, Chi Cheng, Xiaotian Liu, Ruixiang Ni, Zhibo Jin, Chaoyuan Xu, Bo Chen, Siming Wei, Zhongming Xue, Chunlai Wang, Zhanguo Zhao, Chao Materials Science Machine Learning Systems and Control Developing tailored semiconductor heterostructures on demand represents a critical capability for addressing the escalating performance demands in electronic and optoelectronic devices. However, traditional fabrication methods remain constrained by simulation-based design and iterative trial-and-error optimization. Here, we introduce SemiEpi, a self-driving platform designed for molecular beam epitaxy (MBE) to perform multi-step semiconductor heterostructure growth through in-situ monitoring and on-the-fly feedback control. By integrating standard MBE reactors, physics-informed machine learning (ML) models, and parameter initialization, SemiEpi identifies optimal initial conditions and proposes experiments for heterostructure growth, eliminating the need for extensive expertise in MBE processes. As a proof of concept, we demonstrate the optimization of high-density InAs quantum dot (QD) growth with a target emission wavelength of 1240 nm, showcasing the power of SemiEpi. We achieve a QD density of 5 x 10^10 cm^-2, a 1.6-fold increase in photoluminescence (PL) intensity, and a reduced full width at half maximum (FWHM) of 29.13 meV, leveraging in-situ reflective high-energy electron diffraction monitoring with feedback control for adjusting growth temperatures. Taken together, our results highlight the potential of ML-guided systems to address challenges in multi-step heterostructure growth, facilitate the development of a hardware-independent framework, and enhance process repeatability and stability, even without exhaustive knowledge of growth parameters. |
| title | On-Demand Growth of Semiconductor Heterostructures Guided by Physics-Informed Machine Learning |
| topic | Materials Science Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2408.03508 |