Enhancement of the Virtual Metrology Performance for Plasma-Assisted Oxide Etching Processes by Using Plasma Information (PI) Parameters

Park, Seolhye; Jeong, Sangmin; Jang, Yongwon; Ryu, Sangwon; Roh, Hyun-Joon; Kim, Gon−Ho

doi:10.1109/tsm.2015.2432576

Cited by 32 publications

(12 citation statements)

References 11 publications

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“…To this end, various machine learning (ML) models are applied to VM modeling [575,576]. However, this statistical-methodbased VM has shown unsatisfactory prediction accuracy when applied to numerous cases of plasma-aided processes [577].…”

Section: B Data-driven Approaches For Plasma-assistedmentioning

confidence: 99%

“…To develop high-performance VM models, the efficient containment of the 'good information' representing parameters -that is, the parameters representing the process plasma state -is needed rather than the direct application of the ML methodologies. These parameters should efficiently mediate between state variables monitored from the sensors and performance variables, and the specificity of a plasma-assisted process mechanism should be considered [573,577]. Lieberman discussed the importance of the reactions in the plasma volume, sheath, and target surface in terms of the progress of the process reactions, such as etching, deposition, sputtering, and ashing [552].…”

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

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“…The PI variables obtained by OES are named PI-OES. The method to generate PI data from sensor data is described in detail in the later section and previously published papers [ 4 , 12 , 13 ].…”

Section: Development Of Etch Profile Pi-vmmentioning

confidence: 99%

“…Using PI Wall and PI Bulk as the input data of VM, it was demonstrated that the prediction accuracy for nitride thickness increased and the mean-absolute-percentage-error (MAPE) decreased from 0.59% to 0.33%. S. Park et al, (2015) and Y. C. Jang et al, (2019) developed PI-VM model that predicts the etch rate and etch depth with the electron energy distribution function (EEDF) [ 12 , 13 ]. The PI EEDF variable (b-factor) which represents the shape of the EEDF changes the reaction rates of dissociation and ionization in CCP of the SiO 2 trench etch 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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“…In [25], the authors developed a VM model for the plasma-assisted oxide etching process. According to the authors, the prediction accuracy of VM is vital for the model to be reliably used in various monitoring systems in a fab.…”

Section: Related Workmentioning

confidence: 99%

A Realizable Overlay Virtual Metrology System in Semiconductor Manufacturing: Proposal, Challenges and Future Perspective

Tin

Tan

Yong³

et al. 2021

IEEE Access

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Integrated circuits (IC) are fabricated on a wafer through stacked layers of circuit patterns. To ensure proper functionality, the overlay of each pattern layer must be within the tolerance. Inspecting each wafer's overlay is unrealistic and impractical. Hence, wafers are selectively inspected at metrology stations through sampling strategies. With virtual metrology (VM), the metrology quality of the uninspected wafers can be estimated. Motivated by a real-world production environment of a 200mm semiconductor manufacturing plant (fab), a VM to estimate the overlay of the photolithography process is envisioned. Past researches on overlay VM leveraged fault detection and classification (FDC) data to estimate the overlay errors. As such, for fabs in the progress of completing their FDC development for photolithography equipment, a different modeling approach is required to realize an overlay VM that sustains the production line until FDC data can be leveraged for VM. With practical gaps that must be addressed in real fabs, this paper focuses on realizing an overlay VM for the photolithography process without leveraging FDC data. Therefore, the objectives of this paper are two folds: First, to identify the research challenges towards realizing the overlay VM. Second, to propose the future research perspectives of the envisioned overlay VM. Based on the future research perspectives, a two-steps overlay VM modeling approach utilizing data mining techniques is proposed toward realizing the envisioned overlay VM system. The proposed approach first classifies the process stability at the wafer lot level, and subsequently, performs overlay error estimations for wafers in the wafer lots classified with stable process. Linear regression models are proposed to perform overlay error estimations in this work to augment the interpretability of the overlay VM.

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Enhancement of the Virtual Metrology Performance for Plasma-Assisted Oxide Etching Processes by Using Plasma Information (PI) Parameters

Cited by 32 publications

References 11 publications

2022 Review of Data-Driven Plasma Science

2022 Review of Data-Driven Plasma Science

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

A Realizable Overlay Virtual Metrology System in Semiconductor Manufacturing: Proposal, Challenges and Future Perspective

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