Laser Consumable Life Prediction Using Pulse-Based Machine Learning
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Solution Overview
Problem
Current semiconductor exposure technologies face challenges in maintaining resolution due to chromatic aberrations caused by wide spectral linewidths in gas laser apparatuses, leading to inefficient use of consumables and potential production line shutdowns due to unpredictable consumable lifespan.
Innovation Solution
A machine learning method and consumable management apparatus that estimate the life of laser apparatus consumables by creating a learning model based on data from oscillation pulses, allowing for precise degradation tracking and scheduled replacements, thereby extending consumable life and reducing production downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If a gas laser apparatus with wide spectral linewidth is used for exposure, then the laser can be operated with high power and stability, but chromatic aberrations occur resulting in decreased resolution
Solution Approach 1:
The patent segments the broad spectral linewidth into multiple narrow spectral lines using a line narrowing module containing etalons. This segmentation allows the laser to maintain high power while achieving narrow spectral width to minimize chromatic aberrations and improve resolution.
Solution Approach 2:
The patent changes the spectral parameter of the laser by introducing line narrowing elements (etalons) that selectively transmit specific wavelengths. This parameter change narrows the spectral linewidth while maintaining laser power, thereby reducing chromatic aberrations and improving manufacturing precision.
2Loss of time
If consumable replacement is performed based on fixed schedules, then production downtime can be managed, but consumable lifespan cannot be optimized and costs increase
Solution Approach 1:
The patent implements a feedback mechanism by continuously monitoring laser output characteristics (spectral linewidth, power stability) and using this data to predict consumable lifespan. This feedback loop enables dynamic adjustment of replacement schedules, optimizing both production downtime and consumable utilization.
Solution Approach 2:
The patent performs preliminary analysis of degradation trends by monitoring laser parameters over time. This preliminary action allows prediction of consumable failure before it occurs, enabling planned replacements that minimize production downtime while maximizing consumable lifespan.
3Measurement precision
If machine learning models are trained with detailed degradation data, then prediction accuracy improves, but data processing complexity and computational resources increase
Solution Approach 1:
The patent extracts only the most relevant features from raw laser data (spectral linewidth, power stability, oscillation characteristics) for training the machine learning model. This extraction reduces data complexity while maintaining prediction accuracy by focusing on key degradation indicators.
Solution Approach 2:
The patent applies different processing techniques to different data types based on their specific characteristics. Spectral data is processed using Fourier transforms, while temporal data uses statistical analysis. This localized processing approach optimizes computational efficiency while maintaining high prediction accuracy.
Data Source
AI summary
A machine learning method according to a viewpoint of the present disclosure is a machine learning method for creating a learning model configured to estimate the life of a consumable of a laser apparatus, the method including acquiring first life-related information containing data on a parameter relating to the life of the consumable, the data recorded in correspondence with different numbers of oscillation pulses during a period from the start of use of the consumable to replacement thereof, dividing the first life-related information into a plurality of levels each representing the degree of degradation of the consumable in accordance with the numbers of oscillation pulses to create training data, creating the learning model by performing machine learning using the created training data, and saving the created learning model.