Wafer Asset Modeling Using NLP for Predictive Maintenance
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Solution Overview
Problem
Current asset modeling in IoT domains lacks predictive capabilities for asset failures, particularly in semiconductor wafer fabrication, where identifying 'good' or 'bad' recipe processes is challenging, leading to inefficiencies and increased waste.
Innovation Solution
A computing device with a wafer asset modeling module that clusters assets based on static and dynamic properties, converting numeric data to the NLP domain using methods like Bag-of-Words, TF-IDF, or word vectorization to predict malfunctions and determine the quality of semiconductor wafers, enabling corrective actions before failures occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional asset modeling methods are used, then the system is simple to implement, but predictive capabilities for asset failures are lacking
Solution Approach 1:
The patent introduces NLP as an intermediary layer that bridges traditional numeric asset data and predictive modeling. By converting numeric sensor data into natural language representations, the system enables sophisticated predictive analytics without requiring complex direct modeling of raw numeric data, thus improving reliability while managing complexity
Solution Approach 2:
The patent replaces traditional mechanical/numeric data processing systems with an NLP-based cognitive system. Instead of using conventional statistical methods directly on numeric data, the system substitutes an NLP pipeline that transforms numeric data into language representations, enabling more sophisticated predictive capabilities with better interpretability
2Measurement precision
If physical measurements are performed to determine wafer quality, then measurement accuracy is high, but manufacturing time and cost increase
Solution Approach 1:
The patent creates a virtual copy of the physical wafer inspection process by using NLP to represent and analyze wafer data linguistically. Instead of performing physical measurements on every wafer, the system uses language-based models to predict quality outcomes, significantly reducing measurement time while maintaining predictive accuracy
Solution Approach 2:
The system performs preliminary NLP-based quality assessment during the manufacturing process itself, rather than waiting for post-production physical measurements. By converting and analyzing wafer data in real-time using natural language processing, the system can predict quality outcomes before final manufacturing steps are completed, reducing overall cycle time
3Productivity
If all recipe processes are manufactured and tested, then production volume is maximized, but waste increases from identifying bad recipes
Solution Approach 1:
The patent implements a feedback mechanism where NLP models continuously analyze wafer data and provide predictions about recipe quality. This feedback loop allows the system to identify problematic recipes early in the manufacturing process and adjust production accordingly, preventing waste from continuing to produce defective wafers while maintaining overall production volume
Solution Approach 2:
The system performs preliminary identification of bad recipe processes using NLP-based predictive modeling before large-scale production occurs. By analyzing patterns in the data and generating natural language predictions about recipe quality, the system can eliminate or adjust problematic recipes in advance, preventing waste while maintaining productivity from good recipes
Data Source
AI summary
A computing device includes a processor and a storage device. A wafer asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts identifying and clustering a plurality of assets based on static properties of a wafer asset using a first module of the wafer asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the wafer asset using a second module of the wafer asset modeling module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module of the wafer asset modeling module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the wafer asset and/or an event based on past patterns. Prediction information is stored in the storage device.