Use of Optical Emission Spectroscopy Data for Fault Detection of Mass Flow Controller in Plasma Etch Equipment

Kwon, Hyukjoon; Hong, Sang Jeen

doi:10.3390/electronics11020253

Cited by 13 publications

(7 citation statements)

References 25 publications

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“…Therefore, the representative wavelength signal of each element should be selected to analyze the collected OES data. Figure 3 shows the various signals of argon and oxygen in the entire wavelength range, and the two elements are the gases used in this experiment 9,10,11 .…”

Section: Resultsmentioning

confidence: 99%

Virtual metrology scheme for predictive plasma uniformity diagnosis of plasma-enhanced atomic layer deposition process using optical emission spectroscopy

Kim

Na²,

Yun³

2023

Photonic Instrumentation Engineering X

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Plasma is widely used in etching, deposition, ashing, and ion implantation processes in the manufacturing processes of semiconductor and display devices. Recently, the role of plasma in semiconductor processes is becoming important because the pattern with high step coverage and aspect ratio is required as the semiconductor process is scaled. In particular, plasma-enhanced atomic layer deposition (PE-ALD) is recently highlighted with excellent deposition uniformity compared to chemical vapor deposition (CVD) and physical vapor deposition (PVD) technologies 1 . The density and the state of plasma during the process cannot always remain constant and can be changed by process variables and unpredictable process random fluctuations. Since this can cause yield drop and decrease in productivity, the methodology for monitoring and diagnosing the abnormality of plasma in real time during the process is essentially required.In this paper, the plasma monitoring using the optical emission spectroscopy (OES) in PE-ALD equipment to analyze the characteristics of the process variables. Based on the diagnosis results, the optimal process variable values were proposed, and a process performance predictive model is introduced by analyzing the correlation between variables. Based on the modeling results, the modeling output for the final deposited thickness can be predicted by the initial deposition rate, which can be developed as a virtual metrology system application. In addition, by applying this proposed virtual metrology scheme for the advanced process control (APC), it is possible to estimate the process output without whole wafer measuring, thereby improving the time and cost losses and the wafer-to-wafer variation in the semiconductor manufacturing industry.

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Section: Resultsmentioning

confidence: 99%

Virtual metrology scheme for predictive plasma uniformity diagnosis of plasma-enhanced atomic layer deposition process using optical emission spectroscopy

Kim

Na²,

Yun³

2023

Photonic Instrumentation Engineering X

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“…Abnormal equipment conditions were detected using the extended isolation forest approach to demonstrate the ICP of SF 6 , O 2 , and Ar mixtures. 307) A non-invasive in situ plasma monitoring sensor was installed in the main chamber with a small chamber to generate self-plasma OES to show the chemical species residing inside the chamber. 308,309) It has been reported that the end-point detection technique is improved by the CNN-based classifier as compared with the third-order SVM and adaptive boosting (Adaboost) ensemble 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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“…It is well known that faster and more accurate fault detection during the plasma process is quite important to minimize wafer production loss by misprocessing during semiconductor manufacturing. In Reference [3], the authors proposed a new method for achieving more accurate process fault detection from optical emission spectroscopy (OES) data. In the study, under a controlled experimental setup of arbitrarily induced fault scenarios, the extended isolation forest was applied to detect the anomalies in the OES data.…”

Section: The Present Issuementioning

confidence: 99%

Advances in Machine Condition Monitoring and Fault Diagnosis

2022

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In the past few decades, with the great progress made in the field of computer technology, non-destructive testing, signal and image processing, and artificial intelligence, machine condition monitoring and fault diagnosis technology have also achieved great technological progress and played an active and important role in various industries to ensure the efficient and reliable operation of machines, lower the operation and maintenance costs, and improve the reliability and availability of large critical equipment [...]

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Use of Optical Emission Spectroscopy Data for Fault Detection of Mass Flow Controller in Plasma Etch Equipment

Cited by 13 publications

References 25 publications

Virtual metrology scheme for predictive plasma uniformity diagnosis of plasma-enhanced atomic layer deposition process using optical emission spectroscopy

Virtual metrology scheme for predictive plasma uniformity diagnosis of plasma-enhanced atomic layer deposition process using optical emission spectroscopy

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

Advances in Machine Condition Monitoring and Fault Diagnosis

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