Fuzzy Control for Semiconductor Machine Parameter Adjustment
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Solution Overview
Problem
Existing semiconductor manufacturing processes face challenges in accurately adjusting control parameters, leading to over-control, reduced reliability, and increased costs due to reliance on manual methods and single-machine monitoring, which affects yield rates and equipment health.
Innovation Solution
A fuzzy control method is employed to calculate and adjust semiconductor machine control parameters by defining machine measurement and operation parameters, using fuzzy decision-making and membership functions to determine optimal adjustments within acceptable ranges, reducing errors and unnecessary maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual experience control methods are used to adjust manufacturing process parameters, then ease of operation is improved, but manufacturing precision and reliability deteriorate due to over-control and extreme parameter adjustments
Solution Approach 1:
The patent replaces manual experience-based control with an automated statistical control system that uses detection parameters, relationship tables, and automated calculation to determine manufacturing process parameter adjustments, eliminating human judgment errors and over-control issues
Solution Approach 2:
The system implements feedback by continuously monitoring detection parameters, comparing them against statistical control tables, and automatically adjusting manufacturing process parameters based on the calculated relationships, creating a closed-loop control system that improves precision while maintaining ease of operation
2Ease of operation
If statistical manufacturing process control tables are used to monitor single machine parameters, then ease of operation is improved, but manufacturing precision deteriorates due to lack of comprehensive process consideration
Solution Approach 1:
The patent creates a universal relationship table that integrates detection parameters from multiple sources (detection machines, process parameters) and applies it across different manufacturing process machines, enabling comprehensive monitoring and precise control that considers the entire manufacturing process rather than isolated single-machine parameters
3Ease of operation
If conventional parameter adjustment methods are used, then ease of operation is maintained, but reliability decreases due to over-control and extreme parameter adjustments
Solution Approach 1:
The system replaces manual parameter adjustment with automated statistical control that calculates optimal parameter changes based on detection parameter variations and pre-established relationship tables, ensuring reliable and consistent adjustments without human error or over-control
Solution Approach 2:
The system applies partial adjustments by calculating parameter changes based on the actual detected variations rather than applying fixed or extreme adjustment values, making adjustments only as much as needed to correct detected deviations, thereby improving reliability by avoiding excessive parameter changes
Data Source
AI summary
A method of fuzzy control for adjusting a semiconductor machine comprising: providing measurement values from first the “parameter of a pre-semiconductor manufacturing process”, second the “parameter of the semiconductor manufacturing process”, and third the “operation parameter of the semiconductor manufacturing process”; performing a fuzzy control to define two inputs and one output corresponding to the measurement values, wherein the difference between the first and third values, and the difference between the second and third values, forms the two inputs, then from the two inputs one target output is calculated by fuzzy inference; finally, determining if the target output is in or out of an acceptable range. Whereby the target output is the “machine control parameter of the semiconductor manufacturing process” and when within an acceptable range is used for adjusting the semiconductor machine.