Pilot Run Reduction Module for Semiconductor Manufacturing
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Solution Overview
Problem
Manufacturing facilities face inefficiencies due to the high number of pilot runs required for process adjustments, which can be inaccurate and resource-intensive, especially when new equipment is added, maintenance is performed, or products have not been run for a while, leading to suboptimal performance and increased costs.
Innovation Solution
Implementing a pilot run reduction module that determines model intercepts based on a process model, calculates unbiased estimations, and decides when to perform pilot runs, updating the state space model to minimize the number of necessary pilot runs by leveraging real-time and historical data to optimize manufacturing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If pilot runs are performed for each product to optimize manufacturing processes, then manufacturing precision is improved, but productivity deteriorates due to the large number of required pilot runs
Solution Approach 1:
The system performs preliminary analysis using historical data and state space models to predict optimal adjustment data before actual pilot runs are executed. This preliminary action reduces the number of required pilot runs by pre-calculating expected adjustments based on patterns learned from past manufacturing data.
Solution Approach 2:
The system implements a feedback mechanism where historical pilot run data is continuously fed into the state space model to refine predictions. The model learns from past outcomes and improves its ability to predict optimal adjustments, reducing the need for extensive new pilot runs while maintaining or improving precision.
2Productivity
If grouping products is used to reduce the number of pilot runs, then productivity is improved, but manufacturing precision deteriorates because processes are adjusted for groups rather than individual products
Solution Approach 1:
The system segments the analysis by maintaining separate state space models for different products, tools, and manufacturing processes. While pilot runs can be reduced through grouping, each product's specific characteristics are preserved in dedicated models, allowing product-specific optimization without requiring individual pilot runs for every product.
Solution Approach 2:
The system dynamically adjusts model parameters based on product-specific data while using grouping to reduce pilot run requirements. By changing parameters such as model intercepts and adjustment factors based on historical product performance, the system achieves both reduced pilot runs and maintained product-specific precision.
3Productivity
If historical data is used to estimate adjustment data, then productivity is improved by reducing pilot runs, but measurement precision deteriorates due to inaccurate estimates
Solution Approach 1:
The state space model acts as an intermediary between historical data and adjustment data estimation. Rather than directly using historical data to estimate adjustments, the model processes and transforms historical information into more accurate predictions, improving measurement precision while maintaining the productivity benefits of reduced pilot runs.
Solution Approach 2:
The system replaces simple historical data averaging with a sophisticated state space modeling approach. This substitution transforms the estimation process from a basic statistical calculation to a dynamic model-based prediction system that accounts for process variations, tool conditions, and product characteristics, significantly improving estimation accuracy.
Data Source
AI summary
A system and method for reducing pilot runs of run-to-run control in a manufacturing facility calculates an unbiased estimation of an independent model intercept based on a state space model associated with the manufacturing facility. A determination is made as to whether to perform a pilot run in the manufacturing facility. Upon determining that the run is to be performed, an indication that the pilot run is to be performed is generated. Pilot run data is received in response to the pilot run being performed, and the state space model is updated based on the received data.