Semiconductor Reliability Prediction Using AI Electrical Test Data

The patent creates a virtual copy of the physical evaluation process through an AI-based prediction system. Instead of physically testing each semiconductor device, the system uses pre-trained AI models to replicate reliability assessment outcomes, achieving accurate reliability evaluation without time-consuming physical testing or destructive sampling.

The patent replaces the mechanical physical evaluation system with an information-processing system. Rather than using physical testing equipment and manual inspection, the system substitutes AI algorithms that process electrical characteristic data to predict reliability, eliminating the need for physical intervention in the evaluation process.

The patent implements self-service through the AI system's ability to autonomously perform reliability evaluation without human engineer intervention. The pre-trained AI models automatically process electrical characteristic data, make predictions, and generate reliability assessments, enabling the system to serve itself rather than requiring manual analysis for each evaluation.

The patent uses AI models that have learned from extensive training data to replicate reliable evaluation outcomes consistently. The system copies the expertise of experienced engineers into the AI models, allowing automated reproduction of reliable assessments across all mass-produced devices without variation due to human factors.

The patent replaces expensive physical testing resources with computational AI models. Instead of investing in costly testing equipment, consumable test structures, and manual labor for destructive testing, the system uses software-based AI algorithms that process existing electrical characteristic data to predict reliability, dramatically reducing the energy and resource expenditure required for reliability assessment.

The patent substitutes the physical destructive testing system with an information-processing system. Rather than consuming physical test structures and wafer resources through destructive measurement, the system uses AI algorithms that analyze electrical characteristics without physical interference, eliminating the need to sacrifice functional devices for testing and thereby reducing manufacturing costs.