Data-Driven Modeling Using System Integration Scaling Factors and Positioning Performance of an Exposure Machine System

Abstract

A data-driven modeling approach is proposed for using system integration scaling factors and positioning performance of an exposure machine system to build models for predicting positioning errors and for analyzing parameter sensitivity. The proposed approach uses a uniform experimental design (UED), multiple regression (MR), back-propagation neural network (BPNN), adaptive neuro-fuzzy inference system (ANFIS), and analysis of variance (ANOVA). The UED reduces the number of experimental runs needed to collect data for modeling. The MR, BPNN, and ANFIS are used to construct positioning models of an exposure machine system. The significant system integration scaling factors are determined by ANOVA. The inputs to the data-driven model are system integration scaling factors $f_{x}$ , $f_{y}$ , and $f_{q}$ , and the output is the positioning error. The UED was used to collect 41 experimental data, which comprised 0.0595% of the full-factorial experimental data. Performance tests demonstrated the excellent performance of the UED in collecting data used to build the MR, BPNN, and ANFIS data-driven models. The data-driven models can accurately predict positioning errors during validation. In addition, a sensitivity analyses of parameters showed that design parameters $f_{x}$ and $f_{y}$ have the greatest influence on positioning performance.