AI Neural Network for Semiconductor Element Placement
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Solution Overview
Problem
The current semiconductor design process relies heavily on human intuition and experience, leading to inconsistent design quality, high time and financial costs, and inefficiencies in arranging connections between tens to millions of semiconductor elements.
Innovation Solution
A method using artificial intelligence, specifically a neural network model trained through reinforcement learning, to automate the logical design of semiconductors by optimizing the placement of semiconductor elements based on feature and logical design information, reducing design deviations and improving quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If engineers perform semiconductor logical design manually using placement tools based on their experience and intuition, then design flexibility and adaptability are maintained, but design quality consistency deteriorates and time costs increase
Solution Approach 1:
The system enables self-service automation where the semiconductor design process performs placement and routing operations autonomously through AI algorithms. The neural network model automatically determines element positions and connection paths without requiring continuous human intervention, allowing the design system to serve itself and achieve consistent quality while reducing time costs.
Solution Approach 2:
The invention transforms the design process by changing key parameters from human-dependent variables to algorithm-driven variables. The neural network model processes design parameters such as element sizes, connection requirements, and layout constraints through learned patterns, converting subjective engineering intuition into objective, reproducible computational parameters that ensure consistent design quality.
2Productivity
If engineers manually arrange and connect tens to millions of semiconductor elements, then design adaptability is maintained, but ease of operation deteriorates and productivity decreases
Solution Approach 1:
The invention replaces the mechanical manual operation of placing and routing semiconductor elements with an automated computational system. The neural network model substitutes human engineers' manual manipulation with algorithmic processing, automatically determining optimal positions for elements and generating connection paths, thereby dramatically improving productivity while simplifying the operational process.
Solution Approach 2:
The system introduces an intermediary AI layer between the design requirements and the actual placement/routing execution. The neural network model acts as a mediator that translates high-level design specifications into detailed placement and routing decisions, handling the complexity of arranging tens to millions of elements while presenting a simplified interface to users.
3Manufacturing precision
If the semiconductor design process depends on engineer experience and intuition, then design flexibility is maintained, but manufacturing precision deteriorates
Solution Approach 1:
The invention segments the complex design process into distinct computational stages handled by specialized neural network components. The system divides placement and routing tasks into separate processing steps, with each stage receiving specific input parameters and producing defined output results. This segmentation enables precise control over each aspect of the design while managing overall process complexity through modular architecture.
Data Source
AI summary
Disclosed is a method for automating a semiconductor design based on artificial intelligence, which is performed by a computing device. The method may include: receiving feature information and logical design information of a semiconductor element; and training a neural network model to place semiconductor elements in a canvas in an order by a large size based on the feature information and the logical design information.