Regression Analysis

Definition

Regression Analysis is a statistical method for mathematically estimating the relationship between a dependent variable and one or more independent variables. In manufacturing, it is fundamentally used to identify causal relationships between process parameters and quality indicators, and to build predictive models.

Applications in Manufacturing

Quality Prediction and Optimization

• Defect Rate Prediction: Forecasting product defect rates from process variables such as temperature, pressure, and speed
• Yield Improvement: Deriving optimal conditions through analysis of relationships between key process parameters and yield
• Property Prediction: Modeling correlations between raw material mixing ratios and final product properties

Equipment and Process Management

• Energy Consumption Forecasting: Predicting power usage from production volume and operation rates
• Predictive Maintenance: Forecasting remaining useful life or failure timing from equipment sensor data
• Process Anomaly Detection: Identifying process variable combinations that deviate from normal ranges

Key Points

From a manufacturing AI perspective, regression analysis is a traditional pre-deep learning method but remains widely used due to high interpretability. Various techniques including linear regression, polynomial regression, and ridge/lasso regression exist, with the advantage that process engineers can intuitively understand results and apply them to actual process improvements.

Example: In semiconductor etching processes, a regression model with gas flow rate, pressure, and RF power as independent variables and etch depth as the dependent variable is built to derive optimal conditions for achieving target depth.