Virtual Metrology for Etch Profile in Silicon Trench Etching With SF₆/O₂/Ar Plasma

Choi, Jeong Eun; Park, Hyoeun; Lee, Yongho; Hong, Sang Jeen

doi:10.1109/tsm.2021.3138918

Cited by 13 publications

(3 citation statements)

References 34 publications

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“…MFC is an important component for controlling the gas injected into the process chamber. We have confirmed that the gas process significantly affects the process result in the previous studies [ 33 , 34 ]. Data from the abnormal process were acquired followed by the experimental scenario.…”

Section: Data Acquisition and Preprocessingsupporting

confidence: 90%

Generative Adversarial Network-Based Fault Detection in Semiconductor Equipment with Class-Imbalanced Data

Choi

Seol

Kim

et al. 2023

Sensors

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This research proposes an application of generative adversarial networks (GANs) to solve the class imbalance problem in the fault detection and classification study of a plasma etching process. Small changes in the equipment part condition of the plasma equipment may cause an equipment fault, resulting in a process anomaly. Thus, fault detection in the semiconductor process is essential for success in advanced process control. Two datasets that assume faults of the mass flow controller (MFC) in equipment components were acquired using optical emission spectroscopy (OES) in the plasma etching process of a silicon trench: The abnormal process changed by the MFC is assumed to be faults, and the minority class of Case 1 is the normal class, and that of Case 2 is the abnormal class. In each case, additional minority class data were generated using GANs to compensate for the degradation of model training due to class-imbalanced data. Comparisons of five existing fault detection algorithms with the augmented datasets showed improved modeling performances. Generating a dataset for the minority group using GANs is beneficial for class imbalance problems of OES datasets in fault detection for the semiconductor plasma equipment.

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Section: Data Acquisition and Preprocessingsupporting

confidence: 90%

Generative Adversarial Network-Based Fault Detection in Semiconductor Equipment with Class-Imbalanced Data

Choi

Seol

Kim

et al. 2023

Sensors

Self Cite

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“…312) ML-based virtual metrology (VM) predicted the etch profile and depth in deep silicon trench etching with SF 6 /O 2 /Ar plasma by using random forest and XGBoost algorithms, recipe-based equipment status variable identification (SVID) data, and OES data. 313) The etching profiles of Si were estimated from the ratio of the intensities of the oxygen emission to the fluorine lines in the OES data for the SF 6 /C 4 F 8 /O 2 plasma. 314) To monitor the condition of the equipment and examine the potential cause of the fault, SVID was identified from sensor data using k-means and ML methods, neighbors (kNNs algorithms), 315) and naive Bayes classifiers.…”

Section: Fault Detection and Classificationmentioning

confidence: 99%

Science-based, data-driven developments in plasma processing for material synthesis and device-integration technologies

Kambara

Kawaguchi

Lee

et al. 2022

Jpn. J. Appl. Phys.

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Low-temperature plasma processing technologies is essential for material synthesis, device fabrication, and surface treatment. The development of plasma-related products and services requires an understanding of the multiscale complex behaviors of plasma and the hierarchical integration of plasma generation, energy and mass transports through sheath region, surface reactions, and other processes. The importance of science-based and data-driven approaches to controlling systems is argued. The state-of-the-art of deep learning, machine learning, and artificial intelligence in low-temperature plasma science and technology is reviewed. In this review, the requirements and challenges for plasma parameter prediction and processing recipe discovery are asserted by researchers in the fields of material science and plasma processing. It also outlined a science-based, data-driven approach for development of virtual metrology in plasma processes.

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“…As XAI has gained prominence, its application in interpreting machine learning models within semiconductor processes has increased [16][17][18]. We have employed XAI algorithms, including permutation importance and SHapley Additive exPlanations (SHAP), to analyze essential variables that contribute to predictions in machine learning-based models that are used for diagnosing semiconductor plasma processes in APC applications, such as VM and FDC [13,[19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Explainable artificial intelligence-based evidential inferencing on process faults in plasma etching

Choi,

An,

Lee

et al. 2024

J. Phys. D: Appl. Phys.

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The fault detection and classification (FDC) modeling proposed in this study is a research approach that is intended to improve the performance of plasma process models by leveraging optical emission spectroscopy (OES) data containing plasma information (PI) and enhancing model interpretability using explainable artificial intelligence (XAI) algorithms. Status variable identification data that included normal and abnormal states of bias power, pressure, SF6 gas flow, and O2 gas flow were collected during a silicon etching process with SF6, O2 gas plasma. Additional variables were derived from the OES data and included additional PI, such as O and F radicals, which were computed using actinometry, and electron temperature and electron density computed using the line ratio method. By building a high-performance FDC model and interpreting its results using XAI algorithms, we propose solutions to the limitations of the FDC model in semiconductor plasma processes.

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Virtual Metrology for Etch Profile in Silicon Trench Etching With SF₆/O₂/Ar Plasma

Cited by 13 publications

References 34 publications

Generative Adversarial Network-Based Fault Detection in Semiconductor Equipment with Class-Imbalanced Data

Generative Adversarial Network-Based Fault Detection in Semiconductor Equipment with Class-Imbalanced Data

Science-based, data-driven developments in plasma processing for material synthesis and device-integration technologies

Explainable artificial intelligence-based evidential inferencing on process faults in plasma etching

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