Cause analysis of the faults in HARC etching processes by using the PI‐VM model for OLED display manufacturing

Park, Seolhye; Kyung, Yunyoung; Lee, Juyoung; Jang, Yongsuk; Cha, Taewon; Noh, Yeongil; Choi, Younghoon; Kim, Byungsoo; Cho, Taeyoung; Seo, Rabul; Yang, Jungjin; Jang, Yongwon; Ryu, Sangwon; Kim, Gon‐Ho

doi:10.1002/ppap.201900030

Cited by 8 publications

(9 citation statements)

References 25 publications

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“…They have been used to predict various process performances such as etch rate, deposition rate, defect particles, etching profile, deposited thin film quality, and spatial uniformity of the processed results. They have been applied to the control and management of the OLED mass production lines last six years [577][578][579][580][581][582][583].…”

Section: B Data-driven Approaches For Plasma-assistedmentioning

confidence: 99%

“…Developed PI-VM algorithms were applied to the mass production line of the OLED display manufacturing to solve four kinds of problems that occurred in the real field: The defect particle caused process fault prediction [578], root cause analysis of the high-aspect-ratio contact (HARC) etching process faults [579], the management of the mass production discontinuities with a proper application of the in-situ dry cleaning (ISD) [580], and the micro-uniformity problems in the process results [581]. These PI-VM models optimized for each issue have shown enough prediction accuracy to apply for the long-periodic mass production running.…”

Section: B Data-driven Approaches For Plasma-assistedmentioning

confidence: 99%

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2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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Section: B Data-driven Approaches For Plasma-assistedmentioning

confidence: 99%

Section: B Data-driven Approaches For Plasma-assistedmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the observed fab, efficient management of the mass production fab and predictive control of the processes with in-situ monitoring of the plasma processes has been possible [4][5][6][7][8]. Based on these successful application experiences of the PI-VM, we challenged the establishment of a new design rule over the management and control of the plasma etching process itself.…”

Section: Introductionmentioning

confidence: 99%

Data-driven plasma science based plasma etching process design in OLED mass production referring to PI-VM

Park,

Seong,

Park

et al. 2024

Plasma Phys. Control. Fusion

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The production efficiencies of OLED (Organic Light Emitting Diode) displays and semiconductor manufacturing have been dramatically improving with the help of plasma physics and engineering technology by utilizing a process monitoring methodology based on physical domain knowledge. This domain knowledge consists of plasma-heating and sheath physics, plasma chemistry, and plasma-material surface reaction kinetics. They were applied to the PI-VM (Plasma Information based Virtual Metrology) algorithm with the plasma diagnostics and noticeably enhanced process prediction performance by parameterizing plasma information (PI) in various processes of OLED display and semiconductor manufacturing fabs. PI-VM has shown superior process prediction accuracy, which can trace the states of processing plasmas as an application of data-driven plasma science compared to the classical statistics and machine learning-based virtual metrologies; thus, various plasma processes have been managed and controlled with the help of the PI-VM models. More than this, we have adopted the PI-VM model to optimize the patterning architecture and plasma processes simultaneously. The best combination of the etching pattern structure and plasma condition was adjustable based on the detailed understanding of the angular distribution of sputtered atoms from the etching target surface and their interaction with the plasma sheath based on the PI-VM modeling for etching profile failure prediction.

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“…Sensor data refer to data acquired from the optical emission spectroscopy (OES) and voltage-current (VI) probe. It is known that the process results such as etch rate, deposition rate, and fault occurrence can be predicted using the data gathered during the process [ 4 , 5 , 6 , 7 , 8 ]. VM is basically a data process algorithm so it has the advantage of being able to check the process result in real-time during the process.…”

Section: Introductionmentioning

confidence: 99%

Development of Virtual Metrology Using Plasma Information Variables to Predict Si Etch Profile Processed by SF6/O2/Ar Capacitively Coupled Plasma

et al. 2021

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In the semiconductor etch process, as the critical dimension (CD) decreases and the difficulty of the process control increases, in-situ and real-time etch profile monitoring becomes important. It leads to the development of virtual metrology (VM) technology, one of the measurement and inspection (MI) technology that predicts the etch profile during the process. Recently, VM to predict the etch depth using plasma information (PI) variables and the etch process data based on the statistical regression method had been developed and demonstrated high performance. In this study, VM using PI variables, named PI-VM, was extended to monitor the etch profile and investigated the role of PI variables and features of PI-VM. PI variables are obtained through analysis on optical emission spectrum data. The features in PI-VM are investigated in terms of plasma physics and etch kinetics. The PI-VM is developed to monitor the etch depth, bowing CD, etch depth times bowing CD (rectangular model), and etch area model (non-rectangular model). PI-VM for etch depth and bowing CD showed high prediction accuracy of R-square value (R2) 0.8 or higher. The rectangular and non-rectangular etch area model PI-VM showed prediction accuracy R2 of 0.78 and 0.49, respectively. The first trial of virtual metrology to monitor the etch profile will contribute to the development of the etch profile control technology.

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Cause analysis of the faults in HARC etching processes by using the PI‐VM model for OLED display manufacturing

Cited by 8 publications

References 25 publications

2022 Review of Data-Driven Plasma Science

2022 Review of Data-Driven Plasma Science

Data-driven plasma science based plasma etching process design in OLED mass production referring to PI-VM

Development of Virtual Metrology Using Plasma Information Variables to Predict Si Etch Profile Processed by SF6/O2/Ar Capacitively Coupled Plasma

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