Abstract: A system and a method of optimizing an optical proximity correction (OPC) model for a mask pattern of a photo mask is disclosed. A machine learning (ML) based model builder includes an OPC model, measurement data and a random term generator. Random terms are generated in a M-dimensional space by the random term generator. The ML based model builder classifies the random terms to clusters by applying a classifying rule. A representative subset of the random terms is determined among the classified clusters, and the representative subset is added to the OPC model.

Abstract: A system and a method of optimizing an optical proximity correction (OPC) model for a mask pattern of a photo mask is disclosed. A machine learning (ML) based model builder includes an OPC model, measurement data and a random term generator. Random terms are generated in a M-dimensional space by the random term generator. The ML based model builder classifies the random terms to clusters by applying a classifying rule. A representative subset of the random terms is determined among the classified clusters, and the representative subset is added to the OPC model.

Abstract: In a method of manufacturing a lithographic mask of an integrated circuit for semiconductor device manufacturing an optical proximity correction (OPC) process to a layout pattern of the integrated circuit is performed to produce a corrected layout pattern. An inverse lithographic technology (ILT) process to the corrected layout pattern is also performed to enhance the corrected layout pattern to produce an OPC-ILT-enhanced layout pattern of the lithographic mask. A first contour image associated with the OPC-ILT-enhanced layout pattern is generated when the OPC-ILT-enhanced layout pattern of the lithographic mask is projected on a wafer. The features of the generated first contour image are extracted. And a second contour image of a developed photo resist pattern on the wafer associated with the OPC-ILT-enhanced layout pattern as an output of a deep neural network is generated.

Abstract: A method of making a mask includes computing a mask volume correction matrix for a given mask layout to be used to perform a lithography process. The mask volume correction matrix represents a diffraction field for a predetermined thickness of a material of the mask. A simulated mask pattern is computed by applying the mask volume correction matrix to the given mask layout. The simulated mask pattern is provided to a mask making tool.

Abstract: A system and a method of optimizing an optical proximity correction (OPC) model for a mask pattern of a photo mask is disclosed. A machine learning (ML) based model builder includes an OPC model, measurement data and a random term generator. Random terms are generated in a M-dimensional space by the random term generator. The ML based model builder classifies the random terms to clusters by applying a classifying rule. A representative subset of the random terms is determined among the classified clusters, and the representative subset is added to the OPC model.

Abstract: A method of making a mask includes computing a mask volume correction matrix for a given mask layout to be used to perform a lithography process. The mask volume correction matrix represents a diffraction field for a predetermined thickness of a material of the mask. A simulated mask pattern is computed by applying the mask volume correction matrix to the given mask layout. The simulated mask pattern is provided to a mask making tool.

Abstract: A method of making a mask includes computing a mask volume correction matrix for a given mask layout to be used to perform a lithography process. The mask volume correction matrix represents a diffraction field for a predetermined thickness of a material of the mask. A simulated mask pattern is computed by applying the mask volume correction matrix to the given mask layout. The simulated mask pattern is provided to a mask making tool.

Abstract: A method of making a mask includes computing a mask volume correction matrix for a given mask layout to be used to perform a lithography process. The mask volume correction matrix represents a diffraction field for a predetermined thickness of a material of the mask. A simulated mask pattern is computed by applying the mask volume correction matrix to the given mask layout. The simulated mask pattern is provided to a mask making tool.

Abstract: An electromagnetic well logging tool for a wireline logging process, includes a shaft, a transmitter coil mounted on the shaft and configured to generate primary magnetic fields for propagation into a well formation, upon excitation by a first current, a receiver coil mounted on the shaft at a predefined distance from the transmitter coil and configured to receive secondary magnetic fields generated from the well formation, and a bucking coil mounted on the shaft in a concentric arrangement with the receiver coil, wherein the bucking coil is configured to generate magnetic fields at the receiver coil upon excitation by a second current, for cancelling the magnetic fields generated at the receiver coil due to direct coupling between the transmitter and receiver coils.

Abstract: An electromagnetic well logging tool for a wireline logging process, includes a shaft, a transmitter coil mounted on the shaft and configured to generate primary magnetic fields for propagation into a well formation, upon excitation by a first current, a receiver coil mounted on the shaft at a predefined distance from the transmitter coil and configured to receive secondary magnetic fields generated from the well formation, and a bucking coil mounted on the shaft in a concentric arrangement with the receiver coil, wherein the bucking coil is configured to generate magnetic fields at the receiver coil upon excitation by a second current, for cancelling the magnetic fields generated at the receiver coil due to direct coupling between the transmitter and receiver coils.