Substrate Treatment Nozzle Control for Adaptive Film Thickness Correction
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Solution Overview
Problem
Existing substrate treatment apparatuses require manual redevelopment of speed profiles for nozzle movement due to changes in film thickness or pretreatment variations, imposing a burden on operators.
Innovation Solution
A substrate treatment apparatus that uses a learned model to control nozzle movement based on learned target speed information, allowing for automatic adjustment of treatment speed and position, incorporating a nozzle, moving mechanism, storage, and control portion to execute treatments efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual speed profile redevelopment is performed for each pretreatment change, then treatment precision is maintained, but operator burden increases and productivity decreases
Solution Approach 1:
The system automatically generates speed profiles by having the apparatus itself perform the profile development through automated experimentation and learning, eliminating the need for manual operator intervention. The control portion autonomously adjusts nozzle movement speeds based on learned models from previous treatments, enabling the system to self-optimize for different pretreatment conditions without external assistance.
Solution Approach 2:
The patent replaces manual mechanical adjustment of speed profiles with an automated control system that uses learned models and algorithms. The control portion substitutes human operators by automatically generating and adjusting speed profiles based on data from previous treatments, transforming a manual mechanical process into an automated intelligent system.
2Device complexity
If predefined speed profiles are used for nozzle movement, then device complexity is reduced, but adaptability to different pretreatment conditions deteriorates
Solution Approach 1:
The system transitions from static predefined speed profiles to dynamic adaptive profiles. The control portion automatically adjusts movement speeds based on real-time feedback and learned models, allowing the speed profile to change dynamically according to actual treatment conditions and film thickness variations, thereby improving adaptability while maintaining manageable system complexity.
Solution Approach 2:
The system implements feedback mechanisms where the control portion uses data from previous treatments to continuously refine and adjust speed profiles. By monitoring treatment outcomes and using this information to modify subsequent speed profiles, the system adapts to different pretreatment conditions while maintaining a relatively simple control architecture compared to fully custom approaches.
3Productivity
If automated learned model control is implemented, then operator burden is reduced and productivity increases, but device complexity increases
Solution Approach 1:
The control portion serves multiple functions: it executes predefined speed profiles, collects treatment data, generates learned models, and automatically adjusts speed profiles for future treatments. By consolidating these diverse functions into a single multi-functional control system, the patent achieves high automation and productivity while managing overall system complexity through functional integration rather than proliferation of separate components.
Data Source
AI summary
A method of generating additional learning-data, the method uses a substrate treatment apparatus that supplies a treatment solution to a substrate and executes a treatment on the substrate based on an output of a learned model generated by learning learning-data, the method including a step of calculating a difference between a thickness distribution of the substrate before the treatment and a target thickness distribution of the substrate, and acquiring a target treatment amount; a step of executing the treatment on the substrate, and acquiring a treatment amount resulted by the execution of the treatment; a step of determining whether the treatment amount conforms to the target treatment amount; and a step of applying a flag to the treatment amount when the treatment amount is determined not to conform to the target treatment amount, wherein the flag indicates the treatment amount is used as the additional learning-data for the additional learning.