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.