Machine learning enabled optimization of showerhead design for semiconductor deposition process

Jin, Zeqing; Lim, Dahyun Daniel; Zhao, Xueying; Mamunuru, Meenakshi; Roham, Sassan; Gu, Grace X.

doi:10.1007/s10845-023-02082-8

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…ML can control parameters such as deposition rate, temperature, and gas flow rate by optimizing and adjusting the parameters of the fabrication process, and the optimization and adjustment of these parameters can improve the properties of the thin film layer and make the semiconductor device performance more reliable. Jin et al [136] optimized a showerhead design for semiconductor manufacturing to improve airflow uniformity. The authors developed a CAD showerhead parameter model with 30 design variables, including the stem, back plate, porous baffle, and panel dimensions.…”

Section: Process Optimizationmentioning

confidence: 99%

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Zhang 张,

Fu 付

2023

Chinese Phys. B

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Optoelectronic materials are essential for today's scientific and technological development, and machine learning provides new ideas and tools for their research. In this paper, we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices. Then, we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods. We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices, including the methods related to crystal structure, properties (defects, electronic structure) research, materials and devices optimization, material characterization, and process optimization. In summarizing the algorithms and feature representations used in different studies, it is noted that prior knowledge can improve optoelectronic materials design, research, and decision-making processes. Finally, the prospect of machine learning applications in optoelectronic materials is discussed, along with current challenges and future directions. This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field.

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Section: Process Optimizationmentioning

confidence: 99%

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Zhang 张,

Fu 付

2023

Chinese Phys. B

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“…Lu et al employed machine learning to improve the performance of a battery removal platform with a prediction error of less than 10% [ 26 ]. Jin et al applied machine learning to the fabrication of deposited thin film layers in semiconductors and showed that the uniformity of the thickness of the deposited thin film layers was well improved [ 27 ]. Zhang et al applied machine learning methods in the optimization of process parameters for laser-induced plasma micro-machining, with significant improvements in the machining quality of the material [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Processing Optimization of Shear Thickening Fluid Assisted Micro-Ultrasonic Machining Method for Hemispherical Mold Based on Integrated CatBoost-GA Model

et al. 2023

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Micro-electro-mechanical systems (MEMS) hemispherical resonant gyroscopes are used in a wide range of applications in defense technology, electronics, aerospace, etc. The surface roughness of the silicon micro-hemisphere concave molds (CMs) inside the MEMS hemispherical resonant gyroscope is the main factor affecting the performance of the gyroscope. Therefore, a new method for reducing the surface roughness of the micro-CM needs to be developed. Micro-ultrasonic machining (MUM) has proven to be an excellent method for machining micro-CMs; shear thickening fluids (STFs) have also been used in the ultra-precision polishing field due to their perfect processing performance. Ultimately, an STF-MUM polishing method that combines STF with MUM is proposed to improve the surface roughness of the micro-CM. In order to achieve the excellent processing performance of the new technology, a Categorical Boosting (CatBoost)-genetic algorithm (GA) optimization model was developed to optimize the processing parameters. The results of optimizing the processing parameters via the CatBoost-GA model were verified by five groups of independent repeated experiments. The maximum absolute error of CatBoost-GA is 7.21%, the average absolute error is 4.69%, and the minimum surface roughness is reduced by 28.72% compared to the minimum value of the experimental results without optimization.

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A data driven sequential learning framework to accelerate and optimize multi-objective manufacturing decisions

Khosravi,

Olajire,

Raihan

et al. 2024

J Intell Manuf

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Machine learning enabled optimization of showerhead design for semiconductor deposition process

Cited by 5 publications

References 18 publications

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Processing Optimization of Shear Thickening Fluid Assisted Micro-Ultrasonic Machining Method for Hemispherical Mold Based on Integrated CatBoost-GA Model

A data driven sequential learning framework to accelerate and optimize multi-objective manufacturing decisions

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