Neural Network Semiconductor Device Placement Method
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Solution Overview
Problem
The current semiconductor design process relies heavily on engineers' experience and intuition, leading to inconsistent design quality, high time and cost investments, and inefficiencies in placing tens to millions of semiconductor devices while considering their connection relationships.
Innovation Solution
A method using a neural network model trained on characteristic information of semiconductor devices and prohibited area information to optimize the placement of semiconductor devices, reducing the search space and improving learning speed through user input and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based software is used for semiconductor placement, then design consistency can be maintained, but the process requires considerable time and investment
Solution Approach 1:
The neural network model performs placement automatically by learning from training data, eliminating the need for manual engineer intervention. The system serves itself by making placement decisions based on learned patterns, thereby maintaining consistency while dramatically reducing design time and investment requirements.
2Manufacturing precision
If manual placement by engineers is performed, then design quality can be maintained, but it is difficult to efficiently place tens to millions of semiconductor devices
Solution Approach 1:
The patent replaces the mechanical/manual placement process with an AI-based neural network system. The neural network automatically determines optimal placement positions for tens to millions of devices based on learned patterns from training data, achieving both high placement quality and efficiency that manual processes cannot match.
3Speed
If the search space for placement is reduced using prohibited area information, then learning speed is improved, but the flexibility of placement options is reduced
Solution Approach 1:
The system performs preliminary actions by pre-defining prohibited areas based on design constraints and requirements before the actual placement process. This preprocessing step guides the neural network to focus its search on valid regions, improving learning speed while maintaining placement flexibility within the constrained search space.
Data Source
AI summary
Disclosed is a method of placing a semiconductor device, the method being performed by a computing device, the method including: receiving information about a prohibited area designated so that a semiconductor device is not placed; and training a neural network model to place a semiconductor device based on characteristic information of the semiconductor device and the information about the prohibited area.