Artificial Neural Network for Substrate Position Detection

The patent replaces manual rule-based image processing with an artificial neural network system. The neural network automatically learns patterns from training data and makes detection decisions without requiring manual programming of detection rules. This substitution of mechanical rule-based processing with an intelligent neural network system resolves the contradiction by providing both ease of implementation (through automated learning) and high reliability (through adaptive pattern recognition that handles environmental variations).

The patent employs parameter changes by training the neural network with various input parameters such as image characteristics, lighting conditions, and substrate appearances. The network adjusts its internal parameters during training to optimize detection accuracy. This parameter change approach allows the system to adapt to varying environmental conditions while maintaining ease of use, as the parameter optimization is performed automatically during training rather than requiring manual adjustment.

The neural network system performs self-service by automatically learning detection rules from training data without requiring manual programming. The system trains itself on patterns, lighting conditions, and substrate characteristics, then autonomously applies these learned patterns to new situations. This self-service capability provides both simplicity (no manual rule definition needed) and flexibility (automatic adaptation to new conditions), resolving the contradiction between control simplicity and adaptability.

The patent incorporates feedback mechanisms where the neural network continuously refines its detection capabilities based on training data and performance evaluation. The system receives feedback from training results and adjusts its internal parameters accordingly. This feedback loop enables the system to maintain simplicity in operation while developing high flexibility and adaptability through iterative learning, resolving the contradiction between control simplicity and versatility.

The neural network system enables continuous operation by providing rapid and accurate substrate detection without interrupting machine workflow. The automated detection system operates continuously during machine runtime, eliminating the need for manual intervention and reducing downtime. This continuity of useful action resolves the contradiction by maintaining high productivity through automated detection while minimizing time loss from manual operations and machine stops.

The patent applies preliminary action through the training phase, where the neural network pre-learns detection patterns and rules before actual operation. This preliminary training enables the system to perform accurate detection immediately during machine operation without requiring manual rule definition at runtime. The preliminary action of training resolves the contradiction by preparing the system in advance to maintain high efficiency and minimize downtime during actual substrate processing.