The IPMC system can detect possible abnormal signs in advancepredict the RUL of the machine before it fails so that it can effectively avoid unscheduled downtimeprevent enormous losses. The IPMC system also exploits the advantages of cloud computingContainer technology such as rapid deployment, failover,lightweight to enhance its performanceportability.

The DNN models of this technology include a LithoNet, an OPCNet,a layout novelty detection network. LithoNet is a learning-based pre-simulation model for layout-to-SEM contour prediction,OPCnet is a dual network of LithoNet for photomask optimization. Integrated with a well-trained LithoNet, our layout novelty detection network, consisting of a self-attention guided LithoNetan autoencoder, can check if there are layout patterns easily resulting in local distortions in contours of metal lines based on multi-modal (global-local) feature fusion.

This technology aims to automatically extractdefine features from a data-driven perspective through a series of data engineering, machine learningensemble learning technologies via the big data collected from equipment sensorsquality characteristic measurement results. For products that have no quality measurement, a virtual metrology resultpredicted confidence score are provided as a decision-making basis for process control.

The IAMA system with Automatic Virtual Metrology (AVM) technology can enable a 3D printing laser powder bed fusion (LPBF) machine to achieve the world＇s first innovative capability of online layer-to-layer quality predictionfeedback control, which improves the final product quality such as tensile strengthsurface roughnessrealize the goal of nearly Zero Defects of all products.