Semiconductor Process Chamber Digital Twin With Coupled Physics Models
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Solution Overview
Problem
Creating an accurate model of a semiconductor process chamber is challenging due to the complex interaction of various physical phenomena such as fluid dynamics, temperature, plasma behavior, and chemical reactions, which require different timescales and spatial scales for accuracy, making it difficult to combine models of different components effectively.
Innovation Solution
The development of digital twins for semiconductor manufacturing equipment, comprising coupled models of different locations within the process chamber, including AI/ML, HFS, and closed-form solutions, to represent thermal, plasma, fluid dynamics, structural, and chemical reaction characteristics, allowing for the simulation of wafer characteristics and predictive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a model of the entire process chamber is created to evaluate fabrication recipes and component designs, then the ability to simulate and optimize manufacturing processes is improved, but the complexity of combining models of different components with different timescales and spatial scales increases
Solution Approach 1:
The process chamber is divided into multiple discrete locations, each with its own specialized model (AI/ML, HFS, or closed-form). These location-specific models are then coupled together to form the complete digital twin, allowing each component to be modeled at its optimal scale while maintaining overall system accuracy.
Solution Approach 2:
A unified digital twin framework is created that can accommodate multiple types of models (AI/ML, HFS, closed-form solutions) for different locations and physical phenomena. This multi-functional approach allows the same framework to handle thermal characteristics, plasma characteristics, fluid dynamics, structural characteristics, and chemical reactions across various spatial and temporal scales.
2Measurement precision
If different model types (AI/ML, HFS, closed-form) are used for different locations to represent various physical phenomena, then the accuracy of simulating specific physical characteristics is improved, but the difficulty of coupling these diverse models increases
Solution Approach 1:
Different model types are strategically assigned to different locations based on the specific physical phenomena and accuracy requirements at each location. For example, AI/ML models may be used where pattern recognition is critical, HFS models where detailed physical accuracy is needed, and closed-form solutions where computational efficiency is prioritized, optimizing the overall system performance.
Solution Approach 2:
The digital twin framework acts as an intermediary layer that standardizes the coupling interface between diverse model types. This framework handles the integration of AI/ML, HFS, and closed-form models by managing data flow, coordinate transformations, and temporal synchronization, thereby simplifying the coupling process despite model diversity.
3Manufacturing precision
If high-fidelity simulations are performed for accurate modeling, then the precision of process characterization is improved, but the computational time and resources required increase
Solution Approach 1:
The system dynamically adjusts model fidelity and computational parameters based on the specific simulation needs. By using a hierarchy of model types (AI/ML for rapid predictions, HFS for detailed analysis, closed-form for simple relationships), the system can switch between high and low computational resource modes while maintaining adequate accuracy for each specific task.
Data Source
AI summary
Various embodiments herein relate to systems, methods, and media for generating digital twins of semiconductor manufacturing equipment. In some embodiments, a digital twin of a process chamber of semiconductor manufacturing equipment is provided. comprising one or more non-transitory machine readable media comprising logic configured to implement; a first model of a first location of the process chamber; and a second model of a second location of the process chamber, wherein the first model is coupled to the second model, and wherein the first model and the second model are each of a model type that is one of: 1) an AI/ML model; 2) an HFS model; and 3) a closed-form solution, and wherein the first model and the second model each represent a class of physical phenomena that is one of: 1) thermal characteristics; 2) plasma characteristics; 3) fluid dynamics; 4) structural characteristics; and 5) chemical reactions.