Industrial data space application framework for semiconductor wafer manufacturing system scheduling

Abstract

The complex, large-scale semiconductor wafer manufacturing generates substantial diverse data, creating management hurdles and making efficient use of historical scheduling data difficult. To address these challenges, we propose a four-layer application framework for industrial data space for wafer manufacturing system (IDWFS). Firstly, a multi-level model ontology centred on scheduling tasks is constructed to effectively map the evolution of elemental relationships during wafer processing and adaptively change the data organisation. Then, a system architecture for mining the correlation between dynamic and static element data is proposed to fully explore the spatiotemporal correlation relationship of data elements in the processing process. Finally, a scheduling system architecture of “learning + prediction + scheduling” is proposed to fully utilise the scheduling historical domain knowledge and data correlation relationship in semiconductor wafer manufacturing system during the scheduling process. In addition, through three case studies related to the scheduling of semiconductor wafer manufacturing system, IDWFS is effective in heterogeneous data management, coupling relationship mining of element data, logistics scheduling processing, etc., thereby achieving logistics scheduling control of wafer manufacturing system.