Manufacturing Anomaly Detection Using Gaussian Process Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manufacturing systems face challenges in detecting anomalies in product characteristics during lot production, particularly in complex processes like semiconductor fabrication, where defects may not be identified until the final stages, leading to significant quality loss and costs.
Innovation Solution
Implementing a Gaussian process regression model with a bathtub kernel function to analyze data from upstream quality control processes, allowing for predictive distribution generation and anomaly detection, which updates the model and adjusts manufacturing settings based on target values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional quality control procedures are used throughout the manufacturing process, then product quality can be monitored, but anomalies are only detected at final stages leading to significant quality loss and costs
Solution Approach 1:
The patent applies preliminary action by using a Gaussian process regression model to predict product characteristics based on upstream process quality inspection data before the final manufacturing stages are completed. This allows anomaly detection to occur earlier in the production process, enabling preventive actions to be taken before defects are actually created, thus reducing both detection time and quality loss.
2Productivity
If predictive methods are applied to reduce defects, then manufacturing efficiency improves, but the complexity of the system increases
Solution Approach 1:
The patent introduces an intermediary element - a Gaussian process regression model with a bathtub kernel function - that bridges upstream process quality inspection data and final product characteristics. This statistical model acts as a mediator to predict outcomes without requiring direct complex physical models or extensive additional sensing, thus improving manufacturing efficiency while keeping system complexity manageable through the use of established statistical methods.
Data Source
AI summary
The present disclosure describes a computer-implemented method for detecting anomalies during lot production, wherein the products within a production lot are processed according to a sequence of steps that include manufacturing steps and one or more quality control steps interspersed among the manufacturing steps, the method comprising: obtaining process quality inspection data from each of the one or more quality control steps for a first production lot; obtaining product characteristics data for the products in the first production lot after the final step in the sequence; training a Gaussian process regression model using the process quality inspection data and the product characteristics data from the first production lot; generating a predictive distribution of the product characteristics data using the Gaussian process regression model that uses a bathtub kernel function; obtaining process quality inspection data from each of the quality control steps for a second production lot; identifying anomalies in the second production lot using the predictive distribution of the product characteristics data and the process quality inspection data from the second production lot; if no anomalies are detected in the second production lot, updating the Gaussian process regression model using the process quality inspection data from the second production lot; setting target values for one or more values in the process quality inspection data based on the predictive distribution of the product characteristic; and adjusting settings of one or more manufacturing steps based on the target values.