Synopsys, Inc.

Amyn A Poonawala

Benjamin David Painter

Levi D Barnes

Qiliang Yan

Yongfa Fan

Jianliang Li

Jason Jiale Shu

Thomas Christopher Cecil

Thomas Chrisptopher Cecil

Techniques relating to synthesizing masks for use in manufacturing a semiconductor device are disclosed. These techniques include providing, by a processor, a design pattern for a semiconductor device as input to a trained machine learning (ML) model. The techniques further include performing, using the ML Model, a plurality of dilated convolutions relating to the design pattern, and inferring, using the ML model, one or more masks for use in manufacturing the semiconductor device, based on the plurality of dilated convolutions.

Inverse lithography and machine learning for mask synthesis

Techniques relating to synthesizing masks for use in manufacturing a semiconductor device are disclosed. A plurality of training masks, for a machine learning (ML) model, are generated by synthesizing one or more polygons, relating to a design pattern for the semiconductor device, using Inverse Lithography Technology (ILT) (). The ML model is trained using both the plurality of training masks generated using ILT, and the design pattern for the semiconductor device, as inputs (). The trained ML model is configured to synthesize one or more masks, for use in manufacturing the semiconductor device, based on the design pattern ().

One embodiment of the present invention provides a system that determines an assist feature placement within a post-optical proximity correction (post-OPC) mask layout. During operation, the system receives a set of target patterns which represent a set of polygons in a pre-OPC mask layout. The system then constructs a focus-sensitive cost function based on the target patterns, wherein the focus-sensitive cost function represents an amount of movement of post-OPC contours of the target patterns in response to changes in focus condition of the lithography system. Next, the system computes a cost-covariance field (CCF field) based on the focus-sensitive cost function, wherein the CCF field is a two-dimensional (2D) map representing changes to the focus-sensitive cost function due to an addition of a pattern at a given location within the post-OPC mask layout. Finally, the system generates assist features for the post-OPC mask layout based on the CCF field.

Evaluating the quality of an assist feature placement based on a focus-sensitive cost-covariance field

Some embodiments provide techniques and systems to identify locations in a target mask layout for placing assist features. During operation, an embodiment can determine a spatial sampling frequency to sample the target mask layout, wherein sampling the target mask layout at the spatial sampling frequency prevents spatial aliasing in a gradient of a cost function which is used for computing an inverse mask field. Next, the system can generate a grayscale image by sampling the target mask layout at the spatial sampling frequency. The system can then compute the inverse mask field by iteratively modifying the grayscale image. The system can use the gradient of the cost function to guide the iterative modification process. Next, the system can filter the inverse mask field using a morphological operator, and use the filtered inverse mask field to identify assist feature locations in the target mask layout.

Model-based assist feature placement using inverse imaging approach

One embodiment of the present invention provides a system that determines an assist feature placement within a post-optical proximity correction (post-OPC) mask layout. During operation, the system receives a set of target patterns which represent a set of polygons in a pre-OPC mask layout. The system then constructs a focus-sensitive cost function based on the target patterns, wherein the focus-sensitive cost function represents an amount of movement of post-OPC contours of the target patterns in response to changes in focus condition of the lithography system. Note that the contours of the target patterns substantially coincide with the edges of set of the polygons. Next, the system computes a cost-covariance field (CCF field) based on the focus-sensitive cost function, wherein the CCF field is a two-dimensional (2D) map representing changes to the focus-sensitive cost function due to an addition of a pattern at a given location within the post-OPC mask layout. Finally, the system generates assist features for the post-OPC mask layout based on the CCF field.

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Santa Clara, CA, United States of America

Amyn A Poonawala