| 1 |
Next-generation virtual metrology for semiconductor manufacturing: A feature-based framework
Suthar K, Shah D, Wang J, He QP
Computers & Chemical Engineering, 127, 140, 2019 |
| 2 |
Characteristics of a plasma information variable in phenomenology-based, statistically-tuned virtual metrology to predict silicon dioxide etching depth
Jang YC, Roh HJ, Park S, Jeong S, Ryu S, Kwon JW, Kim NK, Kim GH
Current Applied Physics, 19(10), 1068, 2019 |
| 3 |
DeepVM: A Deep Learning-based approach with automatic feature extraction for 2D input data Virtual Metrology
Maggipinto M, Beghi A, McLoone S, Susto GA
Journal of Process Control, 84, 24, 2019 |
| 4 |
DeepVM: A Deep Learning-based approach with automatic feature extraction for 2D input data Virtual Metrology
Maggipinto M, Beghi A, McLoone S, Susto GA
Journal of Process Control, 84, 24, 2019 |
| 5 |
Adaptive virtual metrology for semiconductor chemical mechanical planarization process using GMDH-type polynomial neural networks
Jia XD, Di Y, Feng JS, Yang QB, Dai HH, Lee J
Journal of Process Control, 62, 44, 2018 |
| 6 |
An intelligent virtual metrology system with adaptive update for semiconductor manufacturing
Kang S, Kang P
Journal of Process Control, 52, 66, 2017 |
| 7 |
Virtual metrology modeling of time-dependent spectroscopic signals by a fused lasso algorithm
Park C, Kim SB
Journal of Process Control, 42, 51, 2016 |
| 8 |
An effective procedure for sensor variable selection and utilization in plasma etching for semiconductor manufacturing
Baek KH, Edgar TF, Song K, Choi G, Cho HK, Han C
Computers & Chemical Engineering, 61, 20, 2014 |
| 9 |
An integrated advanced process control framework using run-to-run control, virtual metrology and fault detection
Fan SKS, Chang YJ
Journal of Process Control, 23(7), 933, 2013 |
| 10 |
Real-time virtual metrology and control for plasma etch
Lynn SA, MacGearailt N, Ringwood JV
Journal of Process Control, 22(4), 666, 2012 |
