Towards Multi-Sensor Spectral Alignment Through Post Measurement Calibration Correction

Semiconductor manufactures are increasing reliant on optical emission spectroscopy (OES) to source information on plasma characteristics and process change. However, nonlinearities in the response of OES sensors and errors in their calibration lead to discrepancies in observed wavelength detector response. This paper presents a technique for the retrospective spectral calibration of multiple OES sensors. Underlying methodology is given, and alignment performance is evaluated using OES recordings from a semiconductor plasma process. The paper concludes with a discussion of results and suggests avenues for future work.

On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. The prediction models for VM can be from a large variety of linear and nonlinear regression methods and the selection of a proper regression method for a specific VM problem is not straightforward, especially when the candidate predictor set is of high dimension, correlated and noisy. Using process data from a benchmark semiconductor manufacturing process, this paper evaluates the performance of four typical regression methods for VM: multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), neural networks (NN) and Gaussian process regression (GPR). It is observed that GPR performs the best among the four methods and that, remarkably, the performance of linear regression approaches that of GPR as the subset of selected input variables is increased. The observed competitiveness of high-dimensional linear regression models, which does not hold true in general, is explained in the context of extreme learning machines and functional link neural networks.