ML Deposition Setting Control for Target Film Concentration Profiles
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Solution Overview
Problem
It is difficult for operators of manufacturing systems to identify appropriate deposition process settings that result in a deposited film with a target concentration profile, which is crucial for achieving precise etching of intricate patterns on substrates, leading to inefficiencies and resource consumption.
Innovation Solution
A machine learning model is trained using historical data from prior deposition processes to predict deposition settings that correspond to a target concentration profile, allowing for the identification of settings that satisfy a confidence criterion, thereby reducing the need for extensive experimentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If operators manually identify deposition process settings through experimentation, then they can achieve target concentration profiles for deposited films, but the process consumes excessive time and manufacturing system resources
Solution Approach 1:
The machine learning model is trained in advance using historical deposition process data and corresponding concentration profile measurements. This preliminary training phase enables the model to predict optimal deposition settings for target concentration profiles without requiring real-time experimentation, thus resolving the contradiction between achieving precise concentration profiles and reducing experimentation time
Solution Approach 2:
The system creates a virtual model (machine learning model) that replicates the complex relationship between deposition settings and concentration profiles based on historical data. This digital copy allows operators to query optimal settings instantaneously without physical experimentation, eliminating the time-consuming trial-and-error process while maintaining prediction accuracy
2Manufacturing precision
If operators manually identify deposition process settings through experimentation, then they can achieve target concentration profiles for deposited films, but the process consumes excessive manufacturing system resources
Solution Approach 1:
The machine learning model performs all necessary computational analysis in advance during the training phase using historical data. When deployment occurs, the model provides instantaneous predictions of optimal deposition settings, eliminating the need for resource-intensive real-time experimentation and thereby increasing manufacturing throughput while maintaining concentration profile accuracy
Solution Approach 2:
The system replaces the mechanical experimentation process (physical deposition trials requiring manufacturing system resources) with a computational prediction system. The machine learning model substitutes physical trial-and-error with algorithmic inference, dramatically reducing resource consumption and increasing productivity while achieving the same precision goals
3Manufacturing precision
If extensive experimentation is performed to identify deposition settings, then accurate concentration profiles can be achieved, but latency in the manufacturing process increases
Solution Approach 1:
The machine learning model is trained beforehand on historical deposition data, performing all complex pattern recognition and relationship mapping in advance. During actual manufacturing, the pre-trained model provides immediate predictions of optimal settings for desired concentration profiles, eliminating the latency associated with real-time experimentation while maintaining high precision
Data Source
AI summary
A target concentration profile for a film to be deposited on a surface of a substrate during a deposition process for the substrate at a process chamber of a manufacturing system is identified. Data of the target concentration profile is processed using a model. The model outputs a set of deposition process settings that corresponds to the target concentration profile. One or more operations of the deposition process are performed in accordance with the set of deposition process settings.