Virtual metrology of semiconductor PVD process based on combination of tree-based ensemble model

Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved..

In order to improve the accuracy of semiconductor wafer virtual metrology, and overcome the physical metrology delay of wafer acceptance test, a virtual physical vapor deposition metrology method based on combination of tree-based ensemble models is proposed to conduct online virtual metrology on semiconductor wafer electrical parameters, and use hyperparameter optimization technique to perform model optimization and to achieve real-time alarm on process deviation. This combination of tree-based ensemble model combines Bagging, Boosting, and Stacking techniques. First, based on 4 types of base learner, Random Forest, Extra-Trees, XGBoost, and lightGBM, preliminary virtual metrology is performed on wafer PVD process, and then transforms the predict results of the 4 base learners into meta feature vector as the input of meta learner lightGBM to perform further virtual metrology. The Sequential model-based optimization algorithm is used to improve the accuracy of virtual metrology. First, the initial hyperparameter of the sequential model-based optimization is initialized by using random sampling, then the combination model is approximated by the surrogate model of tree-structured Parzen estimator, and the recommended hyperparameters is obtained by using EI (Expected Improvement), and then the optimized combination model is obtained. Finally, the superiority of the method proposed in this paper is verified by studying the results comparing to the common virtual metrology methods on the PVD process. The experiment shows the result of resistivity metrology using the combination of tree-based ensemble models in the PVD process is significantly better than LASSO regression, partial least squares regression(PLSR), support vector machine(SVR), Gaussian process regression(GPR) and artificial neural network regression(ANN).

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:103

Enthalten in:

ISA transactions - 103(2020) vom: 01. Aug., Seite 192-202

Sprache:

Englisch

Beteiligte Personen:

Chen, Ching-Hsien [VerfasserIn]
Zhao, Wei-Dong [VerfasserIn]
Pang, Timothy [VerfasserIn]
Lin, Yi-Zheng [VerfasserIn]

Links:

Volltext

Themen:

Combination of tree-based ensemble models
Journal Article
Semiconductor
Sequential model-based optimization
Virtual metrology

Anmerkungen:

Date Revised 27.07.2020

published: Print-Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1016/j.isatra.2020.03.031

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM30861853X