Metrology-Based Substrate Defect Analysis for Automated Correction
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Solution Overview
Problem
Conventional manual inspection of substrates for defects is time-consuming, inaccurate, and costly, leading to lower throughput, equipment damage, and increased material waste.
Innovation Solution
A system and method for substrate defect analysis using property data from metrology equipment to identify defect categories and sub-categories, followed by automated corrective actions based on machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to identify substrate defects, then defect detection can be performed, but the process is time-consuming and reduces throughput
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical inspection system that uses images captured by a sensor array to identify defects. The system automatically processes images to detect defects, eliminating the need for manual visual inspection while maintaining or improving detection accuracy and significantly increasing throughput.
2Measurement precision
If manual inspection is used for defect analysis, then defect identification can be performed, but it is inaccurate and leads to increased material waste
Solution Approach 1:
The system replaces manual defect identification with automated image processing that analyzes substrate images to accurately identify defects. This automated approach reduces false positives and negatives, ensuring that only truly defective substrates are discarded, thereby minimizing material waste while maintaining high identification accuracy.
3Reliability
If manual inspection methods are used, then defect detection can be performed, but it is time-consuming and increases processing costs
Solution Approach 1:
The patent implements an automated inspection system that captures images of substrates using a sensor array and processes these images computationally to detect defects. This eliminates the time-consuming nature of manual inspection while maintaining reliable defect detection capability, as the automated system can analyze multiple substrates simultaneously and consistently.
4Productivity
If automated systems are implemented to improve throughput, then productivity increases, but device complexity increases
Solution Approach 1:
The inspection system is designed as a multi-functional automated platform that can handle multiple substrates simultaneously, perform various defect detection tasks, and integrate with existing manufacturing processes. By creating a universal system that performs multiple functions, the patent achieves high throughput without proportionally increasing complexity, as the same hardware infrastructure supports diverse inspection operations.
Data Source
AI summary
A method includes identifying property data of a substrate processed by a substrate processing system. The method further includes identifying, based on a first subset of the property data, a plurality of regions of the substrate corresponding to a first defect category. The method further includes sub-categorizing, based on a second subset of the property data, the plurality of regions of the substrate corresponding to the first defect category into a plurality of defect sub-categories. The method further includes causing, based on one or more of the plurality of regions corresponding to at least one of the plurality of defect sub-categories, performance of a corrective action associated with the substrate processing system.