Physics-Informed AI 智慧淨化 2.0

Physics-Informed AI 智慧淨化 2.0

首創階層式 Physics-Informed AI 淨化，打造智慧半導體新標準。

雲心首創一結合工程、最佳黃金操作點、物理AI及成本歸屬模型等模塊，
將製程尾氣淨化工程系統，以逆物理工程問題(Inverse problem)推論至Local scrubber並至產線製程段，搭配物理PINOs物理資訊運算子模型、MES(製造執行系統)、
成本歸屬模型等數學模型，實現完成一半導體廠務階層式智慧淨化暨成本歸屬系統。

Physics-Informed AI Smart Purification 2.0 

Pioneering Hierarchical Physics-Informed AI Purification – Setting a New Standard for Semiconductor Facilities.

Skybit Solutions pioneers an integrated approach that combines engineering design, optimized golden operating points, Physics-Informed AI, and cost attribution models. By applying Inverse Problem methodologies, we extend process exhaust purification systems from central facilities down to local scrubbers and even to the production line. Leveraging advanced models—including Physics-Informed Neural Operators (PINOs), Manufacturing Execution Systems (MES), and cost attribution frameworks—we deliver the semiconductor industry’s first hierarchical Physics-Informed AI–driven purification and cost attribution system.

階層式平台優點 :

碳儲存(Carbon storage)地底預測模型優點 : 依據碳捕捉案場，所規劃的工程條件，依據存放週期，進行碳存放之濃度與壓力之前預測模型，亦可依配置感測器信號調整模型之部署，以利發揮最大效益。

Subsurface Carbon Storage Prediction Model Advantages
Based on the engineering conditions planned for each carbon capture site and its storage cycle, the model provides predictive insights into carbon concentration and pressure over time. It can also be dynamically adjusted using sensor data inputs, ensuring optimal deployment and maximum effectiveness.