AI Model Integration Platform for Semiconductor Production
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Solution Overview
Problem
The integration of artificial intelligence (AI) technology into semiconductor and display production processes is hindered by a lack of resources and collaboration environments, leading to inefficiencies in developing and deploying AI models, making it difficult for companies to utilize AI effectively.
Innovation Solution
An AI model integration management and deployment system that provides a web service for managing and optimizing AI models, enabling collaboration among developers and allowing for the determination of abnormal processes, and facilitating the deployment of optimized AI models across various production processes through a communication connection between terminals and a server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are developed through external experts or internal experts without a collaboration environment, then AI models can be developed, but it takes a lot of time and the models are difficult to understand and reuse by other developers
Solution Approach 1:
The patent merges multiple AI model development functions into a single integrated online platform that combines model training, optimization, sharing, and collaboration tools. This consolidation allows multiple developers to work together efficiently on the same platform, reducing development time while maintaining model quality through shared resources and collaborative workflows.
Solution Approach 2:
The online platform provides universal access to AI model development resources, allowing any developer to access trained models, optimization algorithms, and collaboration tools through a web interface. This multi-functional platform serves as both a development environment and a sharing repository, eliminating the need for separate systems and reducing overall development time.
2Reliability
If AI models are developed without a shared environment, then individual models can be created, but other developers cannot easily apply or understand these models for various production processes
Solution Approach 1:
The patent implements a model sharing mechanism where trained AI models can be copied and distributed through the online platform. Developers can access pre-trained models, replicate them, and adapt them to different production processes without retraining from scratch. This copying capability significantly enhances model reusability while maintaining the original model's functionality and accuracy.
Solution Approach 2:
The patent segments the AI model lifecycle into distinct modules including model training, optimization, registration, and deployment phases. Each module can be independently accessed and reused by different developers through the platform. This segmentation allows models to be developed in one context and easily applied in different production scenarios, improving adaptability and versatility.
3Adaptability or versatility
If companies lack physical, human, and technical resources, then AI technology introduction is difficult, but companies still want to implement AI in production processes
Solution Approach 1:
The patent introduces an online platform as an intermediary that connects resource-constrained companies with AI model development capabilities. The platform serves as a mediator that provides access to trained models, optimization tools, and collaboration features without requiring companies to invest heavily in their own infrastructure. This intermediary approach enables AI adoption despite limited physical, human, and technical resources.
Solution Approach 2:
The patent implements self-service capabilities where companies can independently access, select, and deploy AI models through the online platform without requiring extensive technical expertise or resource investment. The platform provides automated model optimization, registration, and deployment features that allow companies to implement AI in their production processes using minimal internal resources while maintaining high adaptability.
Data Source
AI summary
An artificial intelligence model integration management and deployment system includes an information management unit configured to receive user information and company information from a user terminal and code received information, an artificial intelligence model management unit configured to receive artificial intelligence model information from the user terminal, map the artificial intelligence model information to information coded by the information management unit, and optimize an artificial intelligence model according to the artificial intelligence model information to determine whether at least one of a plurality of preset processes is abnormal, a list generation unit configured to generate a list of artificial intelligence models optimized by the artificial intelligence model management unit and deploy the list to the user terminal, and a response unit configured to receive call information on the artificial intelligence model included in the list and process data on the plurality of preset processes from the user terminal.