Semiconductor Design Optimization via AI Physical Property Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As semiconductor devices become highly integrated and miniaturized, unintended thermal and electrical characteristics can occur due to complex interactions during design and fabrication, leading to increased demand for accurate process-device simulation environments to reduce experiment costs and improve semiconductor device specifications.
Innovation Solution
A semiconductor design optimization system that predicts physical properties using image data from design drawings by preprocessing design data, generating a physical property prediction model, and optimizing design layouts based on predicted values for each region of the semiconductor device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If semiconductor devices are highly integrated and miniaturized, then device functionality and capacity are improved, but unintended thermal and electrical characteristics occur due to complex interactions
Solution Approach 1:
The system performs preliminary simulation and prediction of thermal and electrical characteristics during the design phase using trained AI models. By analyzing design drawings and predicting physical properties before fabrication, the system identifies potential issues early and allows designers to optimize layouts, preventing reliability problems before they manifest in actual devices.
Solution Approach 2:
The system establishes a feedback loop where predicted physical properties from AI models inform design optimization. The simulation results feed back into the design process, allowing iterative improvement of thermal and electrical characteristics while maintaining high integration and miniaturization benefits.
2Loss of substance
If process-device simulation environments are used to understand phenomena and reduce experiment costs, then experiment costs are reduced, but system complexity increases
Solution Approach 1:
The system replaces complex physical experimentation with AI-based simulation. Trained machine learning models predict thermal and electrical characteristics from design drawings, substituting physical prototyping and measurement with computational analysis. This reduces the need for expensive and time-consuming physical experiments while maintaining prediction accuracy.
Solution Approach 2:
The system creates virtual copies of semiconductor devices through AI simulation. By training models on design drawings and physical property data, the system generates accurate digital representations that replicate real device behavior, allowing virtual testing and optimization without physical prototypes.
3Manufacturing precision
If accurate product specifications are required for semiconductor devices, then product quality is improved, but the need for precise prediction and simulation increases system complexity
Solution Approach 1:
The system transforms the complexity of accurate prediction into manageable parameters by training AI models on specific physical properties (thermal conductivity, electrical characteristics) extracted from design drawings. The models learn to predict these parameters directly from design data, converting complex simulation requirements into streamlined parameter estimation.
Data Source
AI summary
A semiconductor design optimization system that includes: a data base configured to store design data, a training data preprocessing unit configured to preprocess the design data and generate training data, a data learning unit configured to generate a physical property prediction model by training using the training data, a physical property prediction unit configured to generate predicted physical property data including information associated with predicted physical property values for each region of a semiconductor device to be fabricated, wherein the physical property prediction unit is configured to input, into the physical property prediction model, input data including information associated with design drawings of the semiconductor device to be fabricated, and a layout generator configured generate a design layout optimized to distribute the predicted physical property values for each region of the semiconductor device to be fabricated by modifying the design drawings based on the predicted physical property data.