Multi-Scale Simulation with DFT and Non-DFT Modules
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Solution Overview
Problem
Current simulation methodologies for transistor behavior are limited by the hierarchical approach, which fails to accurately capture characteristics like electric field distribution and current crowding effects, requiring a bridge between detailed physics at small scales and larger structures.
Innovation Solution
An adaptive multi-scale simulation tool that combines ab initio and non-ab initio simulation modules, switching between them based on error thresholds and asymptotic values, using density functional theory and other methods to simulate at various scales, reducing computational resources and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If ab initio simulation modules are used to simulate small volumes with high detail, then measurement precision and manufacturing precision are improved, but the volume of the system that can be simulated is limited
Solution Approach 1:
The patent divides the simulation system into multiple simulation modules operating at different scales. Ab initio simulation modules handle small-volume high-precision regions where detailed physics is critical, while non-ab initio modules handle larger-volume regions where approximate models suffice. This segmentation allows the overall system to simulate large volumes while maintaining high precision in critical small regions.
Solution Approach 2:
The patent applies different simulation methodologies to different spatial regions based on local requirements. High-accuracy ab initio methods are applied locally to regions requiring detailed physics (such as areas with strong electric fields or current crowding), while lower-accuracy non-ab initio methods are applied to regions where approximate models are adequate. This local quality approach optimizes the balance between simulation accuracy and computationally feasible volume.
2Volume of stationary object
If non-ab initio simulation modules are used to simulate large volumes, then the volume of the system that can be simulated is improved, but measurement precision and manufacturing precision deteriorate
Solution Approach 1:
The simulation domain is segmented into multiple regions, each handled by appropriate simulation modules. Non-ab initio modules simulate large-volume regions efficiently, while ab initio modules are strategically placed in critical small regions to provide high-precision results where needed, thus maintaining overall simulation accuracy across large volumes.
Solution Approach 2:
Different simulation accuracies are applied to different spatial regions. Non-ab initio methods with lower precision are used for large-volume regions where high precision is not critical, while ab initio methods with high precision are applied locally to critical regions. This creates a spatially varying quality profile that optimizes the trade-off between simulatable volume and simulation accuracy.
3Ease of operation
If manual simulation processes are used, then ease of operation is maintained, but productivity and loss of time worsen due to human intervention and error
Solution Approach 1:
The simulation system automatically selects and coordinates different simulation modules without requiring manual intervention. The controller module autonomously determines which simulation methods to apply in which regions, manages the multi-scale simulation workflow, and integrates results. This self-service capability eliminates manual configuration errors and significantly improves productivity while maintaining ease of use through automated decision-making.
Solution Approach 2:
The system incorporates automated feedback mechanisms that monitor simulation results and adjust the simulation approach accordingly. The controller module receives feedback from simulation outputs and automatically refines the simulation strategy, selecting appropriate modules and parameters without human intervention. This feedback-driven automation improves both productivity and accuracy while reducing manual operational complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate simulation of large systems and reduces computational time by automating the simulation process, minimizing human intervention and error, while providing comprehensive physical insights and reducing the need for empirical experiments.
Implementation Method 1
The first set of one or more simulation modules includes a density functional theory module
Data Source
AI summary
Electronic design automation modules for simulate the behavior of structures and materials at multiple simulation scales with different simulation modules.