Semiconductor Parameter Design Using CVAE Calibration

The patent applies preliminary action by pre-training the CVAE model on extensive TCAD simulation data before actual design tasks. The model learns the complex relationships between device parameters and performance characteristics in advance, so that during actual design, predictions can be made rapidly without requiring real-time TCAD computations. This pre-computation approach stores the computational burden in the training phase, enabling fast inference during deployment.

The patent creates a simplified computational copy of the TCAD simulation system through the CVAE model. Instead of running resource-intensive TCAD simulations for every design iteration, the system uses the trained neural network model that replicates TCAD's predictive capabilities. This copy provides approximately 80% accuracy of full TCAD simulations but with dramatically reduced computational requirements, enabling rapid design exploration.

The patent applies partial action by implementing a calibrated prediction approach that performs verification only when necessary. The CVAE model generates initial predictions quickly, and then selective calibration is applied based on confidence metrics and parameter sensitivity analysis. This avoids performing full verification processes on every parameter adjustment,而是 focusing computational resources on critical parameters and edge cases where verification provides the most value.

The patent implements feedback through the calibration process where model predictions are continuously refined based on comparison with actual TCAD results or experimental data. The system uses the difference between predicted and verified values to adjust subsequent predictions, creating a closed-loop verification system that improves accuracy over time while reducing the frequency of full verification cycles.

The patent substitutes the mechanical TCAD simulation system with a data-driven neural network model. Instead of solving complex partial differential equations through numerical methods, the CVAE model uses learned patterns from training data to predict device behavior. This substitution replaces computationally intensive physics-based simulations with efficient statistical inference, reducing both computational complexity and resource requirements while maintaining acceptable accuracy.

The patent changes the fundamental parameters of the design system by transitioning from deterministic physics-based models to probabilistic data-driven models. The CVAE model incorporates uncertainty quantification and provides predictive distributions rather than single deterministic values. This parameter change allows the system to capture variability and uncertainty in device behavior without requiring exhaustive simulations of all possible parameter combinations.