Combined use of computational intelligence and materials data for on-line monitoring and control of MBE experiments

Meng, Zhuo; Yang, Qian; Yip, P.C.; Eyink, Kurt G.; Taferner, W.T.; Igelnik, B.

doi:10.1016/s0952-1976(98)00024-4

Engineering Applications of Artificial Intelligence

1998

DOI: 10.1016/s0952-1976(98)00024-4

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Combined use of computational intelligence and materials data for on-line monitoring and control of MBE experiments

Zhuo Meng¹,

Qian Yang²,

P.C. Yip³

et al.

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Cited by 3 publications

(2 citation statements)

References 14 publications

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“…It offers an alternative approach where the growth outcomes for an arbitrary set of parameters can be predicted via a trained neural network, which has been applied to extract film thickness and growth rate information 9,10 . Moreover, by enabling the direct adjustment of parameters during material growth, ML-based in situ control can detect and correct any deviation from expected values in a timely manner [11][12][13] . Meng et al have utilized a feedback control to adjust cell temperatures after an amount of film being deposited, thereby capable of regulating Al x Ga 1-x As compositions in situ 13 .…”

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confidence: 99%

“…Moreover, by enabling the direct adjustment of parameters during material growth, ML-based in situ control can detect and correct any deviation from expected values in a timely manner [11][12][13] . Meng et al have utilized a feedback control to adjust cell temperatures after an amount of film being deposited, thereby capable of regulating Al x Ga 1-x As compositions in situ 13 . However, these remain posteriori ML-based approaches, since they require the completion of the growth 10 .…”

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confidence: 99%

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Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots

Shen,

Zhan,

Xin

et al. 2024

Nat Commun

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The applications of self-assembled InAs/GaAs quantum dots (QDs) for lasers and single photon sources strongly rely on their density and quality. Establishing the process parameters in molecular beam epitaxy (MBE) for a specific density of QDs is a multidimensional optimization challenge, usually addressed through time-consuming and iterative trial-and-error. Here, we report a real-time feedback control method to realize the growth of QDs with arbitrary density, which is fully automated and intelligent. We develop a machine learning (ML) model named 3D ResNet 50 trained using reflection high-energy electron diffraction (RHEED) videos as input instead of static images and providing real-time feedback on surface morphologies for process control. As a result, we demonstrate that ML from previous growth could predict the post-growth density of QDs, by successfully tuning the QD densities in near-real time from 1.5 × 1010 cm−2 down to 3.8 × 108 cm−2 or up to 1.4 × 1011 cm−2. Compared to traditional methods, our approach can dramatically expedite the optimization process and improve the reproducibility of MBE. The concepts and methodologies proved feasible in this work are promising to be applied to a variety of material growth processes, which will revolutionize semiconductor manufacturing for optoelectronic and microelectronic industries.

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mentioning

confidence: 99%

mentioning

confidence: 99%

Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots

Shen,

Zhan,

Xin

et al. 2024

Nat Commun

View full text Add to dashboard Cite

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In-situ quantification of the surface roughness for facile fabrications of atomically smooth thin films

et al. 2021

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Single-angle-of-incidence ellipsometry

Zaghloul

2008

Appl. Opt.

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We introduce single angle-of-incidence (SAI) ellipsometry [U.S. patent application 20070024850 (14 July 2006)] as a technique to completely identify, i.e., totally characterize, film-substrate systems. We show that only one measurement of the ellipsometric function rho at one angle of incidence and one wavelength is totally sufficient to determine the optical constant of the film N1, its thickness d, and the substrate's optical constant N2. Obviously, it is also sufficient for characterizing only the film, determining N1 and d, and for characterizing only the substrate, determining N2 and d, as well as for characterizing only bare substrates. An inverse genetic algorithm (IGA) for complete identification is presented that is based on a physical condition of the transparent-film-absorbing-substrate system . This IGA is used to identify the film-substrate system in four separate cases. We show that removing the film thickness from the fitness function of the genetic algorithm and using the defined optimum population size to characterize the film reduces the computational effort from 20,000 to 69 fitness-function calculations; the number of calculations to characterize an absorbing layer is reduced from 80,000 to 180. This is a very significant reduction and is very welcome in real-time applications. An error analysis is presented that shows that the IGA is resilient to, not affected by, random experimental errors and that it gives very good results in the presence of both random and systematic errors of the ellipsometer system. Experimental results are given that also prove the robustness, stability, and high accuracy of the method. We present data only for the SiO2-Si film-substrate system, but the IGA works for any film-substrate system, physical or not.

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Combined use of computational intelligence and materials data for on-line monitoring and control of MBE experiments

Cited by 3 publications

References 14 publications

Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots

Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots

In-situ quantification of the surface roughness for facile fabrications of atomically smooth thin films

Single-angle-of-incidence ellipsometry

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