Virtual metrology modeling of time-dependent spectroscopic signals by a fused lasso algorithm

Abstract

This paper proposes a fused lasso model to identify significant features in the spectroscopic signals obtained from a semiconductor manufacturing process, and to construct a reliable virtual metrology (VM) model. Analysis of spectroscopic signals involves combinations of multiple samples collected over time, each with a vast number of highly correlated features. This leads to enormous amounts of data, which is a challenge even for modern-day computers to handle. To simplify such complex spectroscopic signals, dimension reduction is critical. The fused lasso is a regularized regression method that performs automatic variable selection for the predictive modeling of highly correlated datasets such as those of spectroscopic signals. Furthermore, the fused lasso is especially useful for analyzing highdimensional data in which the features exhibit a natural order, as is the case in spectroscopic signals. In this paper, we conducted an experimental study to demonstrate the usefulness of a fused lasso-based VM model and compared it with other VM models based on the lasso and

Foundations of machine learning for low-temperature plasmas: methods and case studies

“…Alternatively, in the absence of theoretical models, ML has shown great promise for learning multivariable and nonlinear data-driven models from measurements [23][24][25]. Furthermore, ML can facilitate the development of so-called 'soft sensors' (aka virtual metrology [26,27]) that enable real-time diagnostics of plasma and surface properties using accessible and information-rich process measurements [28][29][30][31]. Real-time diagnostics of plasma and surface properties in turn facilitates feedback control of LTP processes, another area where ML can play an important role towards realizing desired LTP processing outcomes on complex interfaces [32][33][34].…”

Enhanced Virtual Metrology on Chemical Mechanical Planarization Process using an Integrated Model and Data-Driven Approach

“…Both linear and non-linear regression techniques have been developed and applied in the VM field. Partial Least-square Regression (PLR) (Geladi & Kowalski, 1986) and lasso regression have been widely used as linear regression approaches and there have been research efforts applying PLR (Hirai & Kano, 2015) and lasso regression (Park & Kim, 2016) to estimate the performance in semiconductor manufacturing. A variant of PLR was derived (Hirai, Hazama & Kano, 2014) to predict the MRR.…”