Abstract:
A layout design system for designing a semiconductor device includes a processor, a storage module storing an intermediate design, and a correction module used by the processor to correct the intermediate design. The intermediate design includes an active region and dummy designs on the active region. Each dummy design includes a dummy structure and dummy spacers disposed at opposite sides of the dummy structure. The correction module is configured to alter widths of regions of at least some of the dummy designs. The corrected design is used to produce a semiconductor device having an active fin, a hard mask layer disposed on the active fin, a gate structure crossing the over the hard mask layer, and a spacer disposed on at least one side of the gate structure. The hard mask layer, and the active fin, are provided with widths that vary due to the dummy designs.
Abstract:
In a method, a dummy gate layer structure and a mask layer are formed on a substrate. The mask layer is patterned to form masks. Spacers are formed on sidewalls of the mask. A dummy gate mask is formed between the spacers. The dummy gate layer structure is patterned using the dummy gate mask to form dummy gate structures. The dummy gate structure is replaced with a gate structure. When the mask is formed, an initial layout of masks extending in a first direction is designed. An offset bias in a second direction is provided for a specific region of the initial layout to design a final layout having a width in the second direction varying along the first direction. The mask layer is patterned according to the final layout to form the masks having a width varying along the first direction.
Abstract:
A semiconductor design automation system comprises a simulator configured to generate simulation data, a recovery module configured to correct a sampling error of the simulation data to generate recovery simulation data, a hardware data module configured to generate real data, a preprocessing module configured to preprocess the real data to generate preprocessed real data, a database configured to store the recovery simulation data and the preprocessed real data, a first graphic user interface including an automatic simulation generator configured to generate a machine learning model of the recovery simulation data and the preprocessed real data and generate predicted real data therefrom, and a second graphic user interface including a visualization unit configured to generate a visualized virtualization process result from the machine learning model.
Abstract:
A semiconductor design automation system comprises a simulator configured to generate simulation data, a recovery module configured to correct a sampling error of the simulation data to generate recovery simulation data, a hardware data module configured to generate real data, a preprocessing module configured to preprocess the real data to generate preprocessed real data, a database configured to store the recovery simulation data and the preprocessed real data, a first graphic user interface including an automatic simulation generator configured to generate a machine learning model of the recovery simulation data and the preprocessed real data and generate predicted real data therefrom, and a second graphic user interface including a visualization unit configured to generate a visualized virtualization process result from the machine learning model.
Abstract:
A semiconductor device includes: active fins protruding from an active layer and extending in a first direction; a gate structure on the active fins extending in a second direction intersecting the first direction; and a spacer on at least one side of the gate structure, wherein each of the active fins includes a first region and a second region adjacent to the first direction in the first direction, and a width of the first region in the second direction is different from a width of the second region in the second direction.
Abstract:
A semiconductor device includes: active fins protruding from an active layer and extending in a first direction; a gate structure on the active fins extending in a second direction intersecting the first direction; and a spacer on at least one side of the gate structure, wherein each of the active fins includes a first region and a second region adjacent to the first direction in the first direction, and a width of the first region in the second direction is different from a width of the second region in the second direction.
Abstract:
A semiconductor design automation system comprises a simulator configured to generate simulation data, a recovery module configured to correct a sampling error of the simulation data to generate recovery simulation data, a hardware data module configured to generate real data, a preprocessing module configured to preprocess the real data to generate preprocessed real data, a database configured to store the recovery simulation data and the preprocessed real data, a first graphic user interface including an automatic simulation generator configured to generate a machine learning model of the recovery simulation data and the preprocessed real data and generate predicted real data therefrom, and a second graphic user interface including a visualization unit configured to generate a visualized virtualization process result from the machine learning model.
Abstract:
A semiconductor design automation system comprises a simulator configured to generate simulation data, a recovery module configured to correct a sampling error of the simulation data to generate recovery simulation data, a hardware data module configured to generate real data, a preprocessing module configured to preprocess the real data to generate preprocessed real data, a database configured to store the recovery simulation data and the preprocessed real data, a first graphic user interface including an automatic simulation generator configured to generate a machine learning model of the recovery simulation data and the preprocessed real data and generate predicted real data therefrom, and a second graphic user interface including a visualization unit configured to generate a visualized virtualization process result from the machine learning model.