Transistor Modeling Using Machine Learning to Predict Electrical Test Data

The patent creates a virtual copy of the electrical testing process through machine learning models. Instead of physically testing each sample, the system trains ML models on existing test data to predict electrical characteristics, effectively copying the testing function in silico. This allows rapid generation of model parameters without repeated physical measurements.

The system performs preliminary machine learning training on historical test data before actual modeling is needed. By pre-training models on existing electrical test data, the system prepares predictive capabilities in advance, so that when new device data arrives, the modeling can be performed rapidly without waiting for extensive new measurements.

The patent replaces physical testing with virtual testing through machine learning. Instead of requiring actual product samples to be tested and potentially discarded, the system creates digital twins through ML predictions, eliminating the need for physical test samples and associated scrap costs.

The system uses existing test data and machine learning algorithms to generate modeling data autonomously, without requiring additional physical testing resources. The ML models self-generate predictions based on training data, eliminating the need for costly physical testing infrastructure and sample materials.

The patent uses machine learning to generate virtual copies of test data that represent the full distribution of possible device characteristics. Instead of physically testing limited samples, the system creates synthetic data through ML models trained on existing data, generating representative samples that capture process variations without requiring proportional physical testing.

The system transforms physical test parameters into predictive model parameters through machine learning. By changing from direct physical measurements to ML-predicted parameters, the system can generate representative data for modeling without being constrained by the limited size of physical test samples, effectively expanding the data representation capability.

The patent implements feedback loops where machine learning models continuously refine their predictions based on comparing predicted values against actual test data. This feedback mechanism allows the system to learn from discrepancies and adjust model parameters, achieving consistency in the face of process variations while maintaining accuracy.

The system changes the approach from directly modeling physical parameters subject to variation to modeling transformed parameters through machine learning. By using ML to map from physical measurements to model parameters, the system creates a more stable transformation that handles process distribution and variation, improving both accuracy and consistency.