Prognostic Maintenance for Semiconductor Equipment Using Neural Networks
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Solution Overview
Problem
Current maintenance methods for semiconductor manufacturing equipment, such as Breakdown Maintenance, Usage-Based, Time-Based, and Condition-Based Maintenance, fail to predict equipment health effectively, leading to inefficient resource allocation and reduced wafer yield due to the complexity of massive data analysis in Fault Detection and Classification systems.
Innovation Solution
A prognostic maintenance method that collects and preprocesses raw data, uses a statistical classification model like Neural Networks to generate health indices, applies dimensional reduction via Principal Component Analysis, and processes these indices with Partial Least Squares Discriminant Analysis to produce health reports, enabling proactive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If engineers use FDC system to analyze output data and generate trend charts, then defect detection capability is improved, but system complexity and data analysis time increase significantly
Solution Approach 1:
The patent extracts only the critical health-related features from the massive raw data output by semiconductor equipment, rather than analyzing all data. The system identifies and extracts key parameters that indicate equipment health status, eliminating unnecessary data processing complexity while maintaining defect detection capability.
Solution Approach 2:
The patent introduces an intermediary prognostic maintenance system that sits between the raw data collection and the final defect detection. This intermediary layer processes raw data through statistical analysis and machine learning models to generate simplified health indicators, which then feed into the defect detection process, reducing the complexity burden on the overall system.
2Measurement precision
If engineers manually analyze trend charts to locate defect causes, then defect identification accuracy is improved, but time consumption and resource waste increase
Solution Approach 1:
The patent implements a self-service prognostic maintenance system that automatically analyzes equipment data and generates health assessments without requiring manual engineer intervention. The system uses statistical classification models and machine learning algorithms to autonomously identify defect causes and predict equipment failures, maintaining high identification accuracy while eliminating time-consuming manual analysis.
Solution Approach 2:
The patent performs preliminary analysis of equipment health status continuously in the background before actual defects occur. By pre-processing data and maintaining updated health models, the system prepares defect identification results in advance, so when defects occur, the analysis is already complete or near-complete, dramatically reducing the time engineers need to spend on active analysis.
3Ease of operation
If quantity-based and time-based maintenance methods are used, then maintenance scheduling simplicity is improved, but equipment failure prevention capability deteriorates
Solution Approach 1:
The patent changes the maintenance scheduling parameter from fixed time intervals or usage counts to dynamic health-based thresholds. The system continuously monitors equipment health parameters and triggers maintenance only when predicted failure probability exceeds a threshold, maintaining scheduling simplicity through automated threshold-based decision rules while dramatically improving failure prevention capability through condition-aware timing.
4Measurement precision
If Condition-Based Maintenance collects monitor data, then maintenance timing accuracy is improved, but ability to predict early health conditions deteriorates due to lack of effective methodology
Solution Approach 1:
The patent performs preliminary health assessment continuously by analyzing monitor data through statistical classification models and machine learning algorithms. The system predicts equipment health trends and potential failures before they manifest as actual problems, enabling maintenance to be scheduled at the optimal time rather than just when conditions deteriorate, thus improving both timing accuracy and early prediction capability.
Solution Approach 2:
The patent implements a feedback mechanism where the prognostic maintenance system continuously monitors equipment data, compares actual performance against predicted health models, and adjusts maintenance predictions accordingly. This closed-loop feedback enables the system to learn from actual equipment behavior and improve early health prediction accuracy over time, maintaining precise maintenance timing while enhancing predictive capability.
Data Source
AI summary
A method for prognostic maintenance in semiconductor manufacturing equipments is disclosed. The said method comprising: collecting a plurality of raw data from the default detection and classification system for equipments, preprocessing the raw data, using the neural network model (NN model) to find a plurality of health indices, generating health information by using the principal component analysis (PCA) to identify the health indices, and using the partial least square discriminated analysis (PLS-DA) to find a health report. The health report provides the engineers with current risk levels of equipments. By the health report, the engineers can initiate prognostic maintenance and repair the equipments early.