Hierarchical System Modeling for Allowed-Region Optimization
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Solution Overview
Problem
Existing systems lack an efficient method to optimize the operation of physical systems by determining optimal input conditions that satisfy both objective functions and constraints, particularly in complex environments like substrate processing systems.
Innovation Solution
A hierarchical system model is created by modeling individual components of the physical system, with an objective function and constraints set by a user, allowing the determination of allowed regions for input data through recursive optimization processing, optimizing the operation of the system by ensuring input data falls within these regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a hierarchical system model is created by modeling individual components with recursive optimization processing, then the manufacturing precision of determining allowed regions for input data is improved, but the device complexity of the optimization system increases
Solution Approach 1:
The system divides the complex optimization problem into hierarchical segments: the system model is decomposed into multiple component models, and the determination of allowed regions is performed recursively from lower-level components to higher-level systems. This segmentation enables precise determination of allowed regions for input data while managing complexity through modular processing.
Solution Approach 2:
The patent implements a nested hierarchical structure where component models are nested within the system model, and allowed region determination is nested recursively through multiple levels. Each component model contains sub-components, and the optimization processing nests deeper into the hierarchy to determine allowed regions at each level, similar to nested dolls.
2Productivity
If recursive optimization processing is performed to determine allowed regions for input data, then the productivity of optimizing physical system operation is improved, but the loss of time for computation increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing the hierarchical system model and component models before optimization is needed. The recursive structure is prepared in advance, allowing the optimization processing to efficiently determine allowed regions by traversing the pre-built hierarchy rather than creating it during each optimization operation.
Solution Approach 2:
The recursive optimization processing implements feedback loops where the determination of allowed regions at each hierarchical level feeds back to refine the understanding of constraints and objectives. This feedback mechanism allows the system to learn from each level of the hierarchy and improve optimization efficiency while managing computational time through intelligent reuse of intermediate results.
Data Source
AI summary
An information processing method, computer-readable medium, and an information processing apparatus optimizes a physical system. The information processing apparatus stores, in a memory circuit, a system model obtained by coupling component models obtained by modeling physical components included in a physical system and modeling the physical system, receives a setting of an objective function for performing an arithmetic operation based on an output value of the system model, determines an allowed region for an input value, in which an output value of the objective function satisfies a predetermined condition, determines an allowed region for an input value of the component model in which an output value of the component model falls within the allowed region, for the component model outputting the input value, and determines an allowed region for an input value of the system model by determining the allowed region for component models constituting the system model.