The present invention discloses an adaptive-learning intelligent scheduling unified computing frame and system for industrial personalized customized production. Based on a deep neural network and reinforcement learning, a most suitable optimization algorithm is selected by automatic decision-making for a global customized production task with an industrial big data module at the bottom as an information basis, and a global optimal static scheduling plane is generated; a current dynamic event in a factory are monitored in real time; if no dynamic event requiring dynamic scheduling optimization is monitored, the global optimal static plan is executed sequentially; when a dynamic event impact requiring dynamic scheduling optimization is monitored, information of the current dynamic event is interpreted and classified, and corresponding optimization algorithms are automatically selected for dynamic scheduling optimization according to different types of dynamic events; and a dynamic scheduling scheme is evaluated by a subsequent module, an optimization scheme is regenerated or a most suitable optimization algorithm is automatically decided based on the scheme according to an evaluation result, and an equipment deployment sequence is generated for an automatic deployment. Considering the features of complicated procedures, a large amount of customization information and the high frequency of diversified dynamic events in personalized customized production, the present invention provides the adaptive-learning intelligent scheduling unified computing frame and system for industrial personalized customized production, that adopt two steps in the three aspects of static scheduling planning, dynamic scheduling planning and equipment deployment based on deep learning, that is, targeted optimization is performed after classification, thus improving the optimization efficiency and effect; and the system better fits the features of personalized customized production, and can effectively improve the efficiency of personalized customized production and minimize manual decision-making costs.

Software for controlling processes in a heterogeneous semiconductor manufacturing environment may include a wafer-centric database, a real-time scheduler using a neural network, and a graphical user interface displaying simulated operation of the system. These features may be employed alone or in combination to offer improved usability and computational efficiency for real time control and monitoring of a semiconductor manufacturing process. More generally, these techniques may be usefully employed in a variety of real time control systems, particularly systems requiring complex scheduling decisions or heterogeneous systems constructed of hardware from numerous independent vendors.