GNN Semiconductor Mesh Simulation for Accuracy and Speed
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Solution Overview
Problem
Current semiconductor device simulations are time-consuming and resource-intensive, often requiring significant computational resources and resulting in less accurate predictions due to the complexity of physical analyses involved.
Innovation Solution
A semiconductor device simulation system utilizing a graph neural network (GNN) that generates and predicts changes in meshes associated with the semiconductor device, improving predictive accuracy and efficiency by using a CPU and RAM to extract nodes and edges from mesh information and apply them to a GNN learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive physical analyses are performed to simulate semiconductor device attributes under variable fabrication and operating conditions, then simulation accuracy is improved, but computational time and resource consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-generating mesh data structures and pre-processing device geometry information before actual simulation runs. The mesh information, including node coordinates and element connectivity, is prepared in advance and stored in an optimized format, allowing the simulation engine to quickly process multiple scenarios without repeated preprocessing overhead.
Solution Approach 2:
The simulation system segments the computational domain into discrete mesh elements (triangles, quadrilaterals, tetrahedrons, etc.) that can be independently processed. This segmentation allows parallel computation across different mesh elements and regions, enabling the system to distribute computational workload and reduce overall simulation time while maintaining accuracy through localized physical analyses.
2Measurement precision
If comprehensive physical analyses are performed to simulate semiconductor device attributes under variable fabrication and operating conditions, then simulation accuracy is improved, but computational resources required increase enormously
Solution Approach 1:
The system applies partial action by selectively performing detailed physical analyses only in regions where they are most needed. The mesh refinement techniques concentrate computational effort in critical areas (such as junction regions or high-field areas) while using coarser meshes in less critical regions, thereby reducing overall computational resource requirements while maintaining accuracy where it matters most.
Solution Approach 2:
The simulation system dynamically adjusts mesh parameters (element size, density, distribution) based on local physical conditions and importance. By changing mesh parameters adaptively, the system optimizes the balance between computational cost and accuracy, using finer meshes only where required by physical phenomena or design requirements, thus reducing total computational resource consumption.
3Productivity
If mesh information is used to predict changes in semiconductor device attributes in response to fabrication and operating conditions, then simulation efficiency is improved, but the complexity of processing and analyzing mesh data increases
Solution Approach 1:
The system extracts and separates essential mesh information (node coordinates, element connectivity, material properties) from the complete simulation model into dedicated data structures. This extraction creates modular, reusable mesh representations that can be independently processed and manipulated, reducing the complexity of handling complete simulation datasets while improving efficiency through focused processing of only the necessary geometric and topological information.
Data Source
AI summary
Systems and methods for simulating a semiconductor device, a method among includes; generating meshes associated with a simulated semiconductor device using a semiconductor device simulator, extracting nodes from information associated with the meshes, extracting edges connected between the nodes using information associated with the meshes, generating graph information in relation to the nodes and edges, applying the graph information to a graph neural network (GNN) learning model, and predicting change in the meshes in response to change in state information applied to the simulated semiconductor device using the GNN learning model.