Abstract: One embodiment of the present invention provides a system that improves lithography performance by correcting for 3D mask effects. During operation the system receives a mask layout that contains etched regions, called shifters, which can have a phase shift relative to other regions. Next, the system chooses a shifter in the mask layout. The system then corrects for 3D mask effects by, iteratively, (a) selecting a region within the shifter, (b) adjusting the phase shift of the selected region in a simulation model to account for 3D mask effects, and (c) modifying the shape of the shifter based on the difference between a desired pattern and a simulated pattern generated using the simulation model.

Abstract: One embodiment of the invention provides a system that expedites or stabilizes convergence in a model-based optical proximity correction (OPC) process. During operation, the system receives a layout for an integrated circuit. Next, the system dissects shapes in the layout into a number of segments, and then runs a number of OPC iterations on the segments to produce an OPC-corrected layout. During each OPC iteration, the system calculates a chrome shift for each segment based on a current layout obtained from the previous iteration, wherein the chrome shift for a segment is measured from the previous position of the chrome edge in that segment. The system then calculates an adjusted chrome shift for each segment based on the newly calculated chrome shift and chrome shift values obtained in one or more previous iterations. Next, the system applies the adjusted chrome shift values to the current layout to obtain an updated layout.

Abstract: One embodiment of the present invention provides a system that identifies an area in a mask layout which is likely to cause manufacturing problems. During operation, the system creates an on-target process model that models a semiconductor manufacturing process under nominal process conditions. The system also creates one or more off-target process models that model the semiconductor manufacturing process under one or more arbitrary process conditions. Next, the system computes a process-sensitivity model using the on-target process model and the off-target process models. The system then identifies a problem area in the mask layout using the process-sensitivity model. Identifying the problem area allows it to be corrected, which improves the manufacturability of the mask layout. Moreover, using the process-sensitivity model to identify the problem area reduces the computational time required to identify the problem area.

Abstract: Complex layout features, especially two-dimensional (2D) features such as jogs and corners, are more susceptible to photo-resist pinching and bridging, even with the use of optical proximity correction. These problems may arise due to unrealistic targets, e.g. square corners, thereby resulting in excessively aggressive correction in the vicinity of these 2D features. To provide a more realistic target, an aerial image can be sampled and its gradient computed at evaluation points on the 2D feature. The aerial image contains spatial information about the local pattern and the interaction of the pattern with the manufacturing process. This information can be used to predict a feasible shape or curvature for the 2D feature. The predicted shape can then be used to retarget the 2D feature based on realistic process capabilities.

Abstract: One embodiment of the present invention provides a system that identifies an area in a mask layout which is likely to cause manufacturing problems. During operation, the system creates an on-target process model that models a semiconductor manufacturing process under nominal process conditions. The system also creates one or more off-target process models that model the semiconductor manufacturing process under one or more arbitrary process conditions. Next, the system computes a process-sensitivity model using the on-target process model and the off-target process models. The system then computes a gradient-magnitude of the process-sensitivity model. Next, the system identifies a problem area in the mask layout using the gradient-magnitude of the process-sensitivity model. Identifying the problem area allows it to be corrected, which improves the manufacturability of the mask layout.