Semiconductor Temperature Correction Model for Deposit-Driven Film Drift
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Solution Overview
Problem
Existing semiconductor manufacturing apparatuses face challenges in maintaining accurate temperature control due to the accumulation of deposits on inner walls, leading to deviations in film thickness despite set temperature adjustments, which are not effectively addressed by existing temperature correction methods.
Innovation Solution
A temperature correction information calculation device that includes a model storage unit, a learning determination unit, and a model learning unit to automatically switch on or off the learning function based on film forming results, updating the model to improve temperature correction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the set temperature is controlled according to the process recipe, then the heat treatment can be performed at the prescribed temperature, but the film thickness becomes inaccurate due to temperature deviation caused by cumulative deposits on the inner wall
Solution Approach 1:
The patent implements a feedback mechanism by measuring the actual film thickness formed during heat treatment and using this measurement to update the temperature correction model. The system continuously monitors the relationship between set temperature, cumulative film thickness, and actual film thickness, then adjusts the temperature correction values accordingly to maintain accurate film thickness control despite deposit accumulation on the inner wall.
Solution Approach 2:
The patent changes the temperature parameter dynamically by introducing temperature correction values that are applied to the set temperature from the process recipe. These correction values are adjusted based on the cumulative film thickness and actual film thickness measurements, allowing the system to compensate for temperature deviations caused by deposit accumulation and maintain reliable film thickness control.
2Manufacturing precision
If the temperature correction model is continuously updated to improve accuracy, then the film thickness control improves, but the system complexity and computational requirements increase
Solution Approach 1:
The patent applies partial action by selectively updating the temperature correction model only when necessary - specifically when new film thickness measurements are obtained. Rather than continuously complex computations, the system performs model updates at discrete intervals based on actual measurements, reducing computational overhead while maintaining accuracy improvements.
3Manufacturing precision
If the learning function is always active to adapt to changing conditions, then the temperature correction accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The patent implements periodic action by activating the learning function at specific intervals - namely when film thickness measurements are obtained after heat treatment cycles. The model is updated periodically based on accumulated measurement data rather than continuously, allowing the system to adapt to changing conditions while minimizing processing time and computational load during operation.
Data Source
AI summary
A temperature correction information calculation device includes a model storage unit that stores a model for generating temperature correction information in which a temperature correction value is associated with a cumulative film thickness on an inner wall of a semiconductor manufacturing apparatus that forms a film on a processing target object by a heat treatment at a set temperature corrected according to the cumulative film thickness; a learning determination unit that determines whether or not to update the model when a film forming result by the heat treatment is obtained; a model learning unit that updates the model based on the film forming result when the learning determination unit determines to update the model; and a temperature correction information generation unit that generates the temperature correction information using the model updated by the model learning unit and corrects the set temperature by the temperature correction information.