Oxide Semiconductor Inspection Device Using Neural Network Analysis

The patent applies preliminary action by performing oxygen supply treatment before heat treatment or plasma treatment. This pre-treatment introduces oxygen into the oxide semiconductor layer in advance, creating a reservoir of oxygen that prevents defect formation when subsequent heating or plasma processing releases oxygen. The oxygen supply treatment is conducted at a lower temperature (room temperature to 200°C) before the main processing step, ensuring the oxide semiconductor is pre-saturated with oxygen to withstand the upcoming thermal or plasma stress.

The patent implements beforehand cushioning by introducing oxygen into the oxide semiconductor layer prior to heat treatment or plasma treatment. This oxygen acts as a cushion or buffer that compensates for the oxygen loss during subsequent high-temperature or plasma processing. The oxygen supply treatment creates a protective oxygen reservoir that cushions against the formation of oxygen vacancies and prevents carrier mobility degradation, thereby protecting the oxide semiconductor quality during manufacturing processes.

The patent introduces an intermediary approach by using machine learning algorithms as a mediator between conventional inspection images and defect detection. Instead of directly analyzing inspection images with simple threshold methods, the system employs trained machine learning models (such as convolutional neural networks) that have been pre-trained on labeled defect data. This intermediary AI layer enhances the detection precision of oxygen vacancy defects while building upon the existing inspection infrastructure, achieving high precision without completely replacing the conventional inspection workflow.

The patent applies parameter changes by transforming the inspection approach from conventional direct imaging to machine learning-enhanced image analysis. The system changes the analytical parameters by using trained models that can detect subtle patterns and features invisible to conventional methods. The machine learning models process inspection images with enhanced feature extraction capabilities, changing the detection parameters from simple intensity thresholds to complex pattern recognition, thereby significantly improving oxygen vacancy detection precision while maintaining workflow integration.

The patent applies preliminary action by performing oxygen supply treatment at low temperatures (room temperature to 200°C) before heat treatment or plasma treatment. This pre-treatment introduces and stabilizes oxygen in the oxide semiconductor layer at low temperatures, ensuring the material is pre-saturated with oxygen to withstand subsequent high-temperature processing. The low-temperature oxygen supply preserves the advantages of oxide semiconductors while preparing the material for high-temperature steps.

The patent implements beforehand cushioning by introducing oxygen into the oxide semiconductor layer prior to heat treatment or plasma treatment. This oxygen acts as a cushion or buffer that compensates for the oxygen loss during subsequent high-temperature or plasma processing. The oxygen supply treatment creates a protective oxygen reservoir that cushions against the formation of oxygen vacancies and prevents carrier mobility degradation, thereby protecting the oxide semiconductor quality during manufacturing processes.