3D Semiconductor Equipment Routing for Integrated Pipe Layout

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Solution Overview

Problem

Current semiconductor manufacturing plant designs face significant challenges due to the lack of integrated design and review, leading to increased reconstruction costs and prolonged pipe design and modification periods, with manual 2D drawings resulting in inefficiencies and material wastage.

Innovation Solution

A machine learning-based automatic routing method and apparatus that defines 3D connection points between semiconductor equipment and ancillary equipment, using a machine learning algorithm to optimize material usage and reduce construction time by accurately calculating material lengths.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If manual 2D drawing methods are used for pipe design, then each person can generate designs according to their field, but integrated design and review cannot be performed leading to increased reconstruction costs

Engineering Contradiction:

Improveease of pipe designVSAvoiddesign integration and accuracy

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The patent merges multiple manual 2D drawing processes into a single integrated 3D routing system. The machine learning algorithm automatically generates unified routing paths that coordinate pipes, wiring, and facilities across the entire manufacturing plant, eliminating the need for separate manual designs by different stakeholders and ensuring consistent integration throughout the project.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent replaces manual mechanical drawing processes with an automated machine learning-based routing system. The algorithm automatically calculates optimal paths for utilities based on 3D spatial relationships and constraints, substituting human manual design with computational intelligence that provides more consistent and integrated results.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If manual 2D drawing methods are used, then design processes can be performed by multiple stakeholders, but pipe design interference and conflict increase leading to prolonged modification periods

Engineering Contradiction:

Improvestakeholder design capabilityVSAvoidpipe design modification period

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent performs preliminary 3D routing calculations and conflict detections during the design phase before construction begins. The machine learning algorithm automatically identifies potential interference between pipes, wiring, and facilities and resolves conflicts in advance, eliminating the need for prolonged on-site modifications and rework during construction.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates continuous feedback loops that automatically detect conflicts and constraints in real-time during the routing process. When potential interference is detected between utilities, the algorithm automatically adjusts paths or generates resolution recommendations, providing immediate feedback that prevents conflicts from escalating into time-consuming modification periods.

Inventive Principle:

Principle #23Feedback

3Productivity

If manual design methods are used without 3D coordination, then material lengths cannot be calculated in advance, but this leads to material wastage and increased building costs

Engineering Contradiction:

Improvedesign speedVSAvoidmaterial wastage

Core Design Contradiction:

ProductivityVSLoss of substance

Solution Approach 1:

The patent transitions from 2D planar designs to 3D spatial routing, enabling accurate calculation of material lengths in three-dimensional space. The machine learning algorithm computes precise path lengths for pipes, wiring, and facilities based on 3D coordinates and spatial relationships, allowing exact material quantity calculations before construction and eliminating wastage from on-site cutting and discarded materials.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

4Ease of manufacture

If traditional manual routing methods are used, then construction can proceed with basic planning, but building costs increase due to reconstruction and material wastage

Engineering Contradiction:

Improveconstruction feasibilityVSAvoidbuilding material cost

Core Design Contradiction:

Ease of manufactureVSQuantity of substance

Solution Approach 1:

The patent changes key parameters from manual 2D design to automated 3D machine learning-based routing. This parameter transformation enables the system to optimize material usage by calculating exact lengths required for utilities based on actual 3D spatial relationships and equipment positions, thereby reducing material wastage and lowering overall building costs while maintaining construction feasibility.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12541633B2Machine learning based automatic routing method and apparatus for semiconductor equipment

Publication Date: 2026.02.03 RC TECH

AI summary

The present disclosure relates to a machine learning-based automatic routing method and apparatus for semiconductor equipment, and the machine learning-based automatic routing method for semiconductor equipment according to one embodiment of the present disclosure includes: a first operation of disposing semiconductor equipment and ancillary equipment; a second operation of recognizing connection points (points of connection, POC) which are three-dimensional (3D) coordinates of the semiconductor equipment and the ancillary equipment; and a third operation of generating an optimal path which connects the connection points (POC) using a machine learning algorithm.