Automatic Data Quality Evaluation for the AVM System

Abstract

This paper proposes the schemes of automatic process and metrology data-quality evaluations for the automatic virtual metrology (AVM) system. Firstly, principal component analysis is applied to extract data features of all the collected equipment process data; then Euclidean distance is utilized to unify all the principal components into a single index denoted by process data quality index (DQI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> ) for evaluating the quality of process data. Second, adaptive resonance theory 2 (ART2) and normalized variability are applied to define the metrology data quality index (DQI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> ) for appraising the quality of metrology data. The thresholds of both DQI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> and DQI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> are also defined and can be adaptively calculated. The DQI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> and DQI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> data quality evaluation schemes are well suited for the AVM systems of the semiconductor and thin film transistor-liquid crystal display industries to online, real-time, and automatically evaluate the quality of all the collected process and metrology data. As such, abnormal data will not be adopted for VM model training or tuning and VM conjecture accuracy can be maintained.