Circuit Board Conductor Pattern Search Using Local Teacher Data
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Solution Overview
Problem
Optimizing conductor patterns on circuit boards is time-consuming, and desired characteristics may not be achieved even after optimization.
Innovation Solution
A search program and information processing apparatus that divide a circuit board into local regions, perform local and overall searches using teacher data to optimize conductor arrangements, and combine initial and improved structures to find a desired pattern in a short time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional optimization methods are used to optimize conductor patterns, then pattern optimization is achieved, but it takes a long time and desired characteristics may not be obtained
Solution Approach 1:
The patent divides the conductor pattern optimization problem into multiple local regions, each optimized independently with local teacher data, rather than optimizing the entire pattern globally. This segmentation allows parallel processing and reduces the time required while maintaining optimization quality.
Solution Approach 2:
The patent prepares teacher data in advance that encodes desired frequency characteristics and optimization goals. This preliminary preparation of reference data enables the optimization process to converge faster by providing predefined guidance, reducing the time needed to achieve desired characteristics.
2Productivity
If the number of optimization calculations is reduced to save time, then search speed improves, but the quality of pattern optimization may deteriorate
Solution Approach 1:
The patent introduces teacher data as an intermediary that guides the optimization process. This teacher data acts as a mediator between the optimization algorithm and the desired outcome, providing direction and constraints that ensure high-quality results even when the number of calculations is reduced.
Solution Approach 2:
The patent changes the parameter representation by using teacher data with specific frequency characteristic parameters to guide optimization. This parameter transformation allows the system to achieve better results with fewer iterations by optimizing based on pre-defined characteristic parameters rather than brute-force calculation.
Data Source
Figure 1A~1B
Figure 2
Figure 3A~3B
AI summary
According to an aspect of the embodiments, a search program causing a computer to execute a process includes first processing of preparing a basic structure that includes a plurality of cells that serve as units of arrangement and non-arrangement of a target object in a predetermined region, second processing of dividing the basic structure into a plurality of local regions each of which includes a plurality of cells, third processing of, for each of the plurality of local regions, when an initial structure, which is a combination pattern of arrangement and non-arrangement of the target object included in the local region in the basic structure, is set to a combination pattern of arrangement and non-arrangement of the target object different from the initial structure, performing a search for arrangement and non-arrangement in the cells such that characteristics of the predetermined region as a whole are improved and searching for an improved structure in which the characteristics are improved, and fourth processing of, for each of the plurality of local regions, creating a plurality of combinations of the initial structure and the improved structure in the predetermined region as pieces of teacher data by designating any of the initial structure and the improved structure, and searching for arrangement and non-arrangement of the target object in the cells of the predetermined region by using the plurality of pieces of teacher data.