A Standard Framework for AI-Driven Optimizations in Various Complex Domains

Deploying AI in production is a challenging task that necessitates the integration of various standalone software solutions, including Manufacturing Execution System, Simulation, and AI. The process of discussing, defining, and implementing interfaces requires both time and financial resources. In this paper, we propose a standardized interface framework that not only facilitates the training of Deep reinforcement learning agents in the semiconductor industry but also offers flexibility, extensibility, and configurability for other use cases and domains. To achieve this, we present OpenAPI interface definitions that enable automatic code generation, along with a set of data schemas that adequately describe the semiconductor domain for most use cases. By adopting this framework, the coupling between software modules is reduced, enabling researchers to work independently on their solutions. Moreover, it ensures compatibility among modules, allowing for plug and play functionality, and simplifies the deployment process in production.