Synopsys, Inc.

Asml Netherlands B.v.

Yongfa Fan

Yen-Wen Lu

Feng Chen

Qiliang Yan

Jen-Shiang Wang

Mu Feng

Qian Zhao

Leiwu Zheng

Jensheng Huang

Benjamin David Painter

Jianliang Li

Levi D Barnes

Amyn A Poonawala

Hongbo Zhang

Ebo H Croffie

Xi Chen

Ya Luo

Qiaolin Zhang

Yu Zhao

Jiao Liang

Jin Cheng

Lin Zhang

Thomas Schmoeller

Yi-Yin Chen

Bradley J Falch

Qian Ren

Dianwen Zhu

Peter Brooker

Jiao Huang

Yunan Zheng

Ziyang Ma

Xin Guo

Chenji Zhang

A method for training a patterning process model, the patterning process model configured to predict a pattern that will be formed by a patterning process. The method involves obtaining an image data associated with a desired pattern, a measured pattern of the substrate, a first model including a first set of parameters, and a machine learning model including a second set of parameters; and iteratively determining values of the first set of parameters and the second set of parameters to train the patterning process model. An iteration involves executing, using the image data, the first model and the machine learning model to cooperatively predict a printed pattern of the substrate; and modifying the values of the first set of parameters and the second set of parameters such that a difference between the measured pattern and the predicted pattern is reduced.

Method for training or using a process model for determining a pattern in a patterning process

A method involving determining an etch bias for a pattern to be etched using an etch step of a patterning process based on an etch bias model, the etch bias model including a formula having a variable associated with a spatial property of the pattern or with an etch plasma species concentration of the etch step, and including a mathematical term including a natural exponential function to the power of a parameter that is fitted or based on an etch time of the etch step; and adjusting the patterning process based on the determined etch bias.

Etch bias characterization and method of using the same

Systems and methods for determining one or more characteristic metrics for a portion of a pattern on a substrate are described. Pattern information for the pattern on the substrate is received. The pattern on the substrate has first and second portions. The first portion of the pattern is blocked, for example with a geometrical block mask, based on the pattern information, such that the second portion of the pattern remains unblocked. The one or more metrics are determined for the unblocked second portion of the pattern. In some embodiments, the first and second portions of the pattern correspond to different exposures in a semiconductor lithography process. The semiconductor lithography process may be a multiple patterning technology process, for example, such as a double patterning process, a triple patterning process, or a spacer double patterning process.

Determining metrics for a portion of a pattern on a substrate

A method and apparatus of a novel full chip edge-based mask three-dimensional (3D) model for performing photolithography simulation is described. The method applies a thin mask model to a mask design layout to create a thin mask transmission. The method generates a thick mask model that has a plurality of edge-based kernels. The method applies the thick mask model to the mask design layout to create a mask 3D residual. The method combines the thin mask transmission and the mask 3D residual to create a mask 3D transmission.

Edge-based full chip mask topography modeling

Computer-implemented techniques for pixel source optics calculations using spatial coherence are disclosed. Pixelated sources are used for source-mask co-optimization to enhance semiconductor lithography. Calculation of a partially coherent imaging system is used for optical-lithography simulation. The spatial coherence property of neighboring source points is used to reduce imaging calculation complexity. Two or more neighboring points are treated as one pseudo-spatially coherent area element.

Performing image calculation based on spatial coherence

One embodiment of the present invention provides techniques and systems for modeling mask errors based on aerial image sensitivity. During operation, the system can receive an uncalibrated process model which includes a mask error modeling term which is based at least on an aerial image sensitivity to mask modifications which represent mask errors. Next, the system can fit the uncalibrated process model using measured CD data. Note that the mask error modeling term can also be dependent on the local and/or long-range pattern density. In some embodiments, the mask error modeling term can include an edge bias term and a corner rounding term. The edge bias term can be based on the sensitivity of the aerial image intensity to an edge bias, and the corner rounding term can be based on the sensitivity of the aerial image intensity to a corner rounding adjustment.

Modeling mask errors using aerial image sensitivity

One embodiment of the present invention provides techniques and systems for modeling mask errors based on aerial image sensitivity. During operation, the system can receive an uncalibrated process model which includes a mask error modeling term which is based at least on an aerial image sensitivity to mask modifications which represent mask errors. Next, the system can fit the uncalibrated process model using measured CD data. Note that the mask error modeling term can also be dependent on the local pattern density. In some embodiments, the mask error modeling term can include an edge bias term and a corner rounding term. The edge bias term can be based on the sensitivity of the aerial image intensity to an edge bias, and the corner rounding term can be based on the sensitivity of the aerial image intensity to a corner rounding adjustment.

Method and apparatus for using aerial image sensitivity to model mask errors

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 a system for accurately and efficiently modeling chemically amplified resist. During operation, the system can determine a quenched acid profile from an initial acid profile by applying multiple quenching models which are associated with different acid concentration ranges to the initial acid profile. One quenching model may be expressed as H=H&#x2212;B, where H is an acid profile after quenching, His an acid profile before quenching, and Bis an initial base quencher profile. Another quenching model may be expressed as H=k&#xb7;H, where k is a constant. Next, the system can apply a smoothing kernel to the quenched acid profile to obtain a quenched-and-diffused acid profile. The smoothing kernel can generally be any weighted averaging function. The quenched-and-diffused acid profile can then be used to predict shapes that are expected to print on the wafer and to perform resolution enhancement techniques on a layout.

Method and apparatus for modeling chemically amplified resists

A method is described herein for predicting lateral position information about a feature represented in an integrated circuit layout for use with an integrated circuit fabrication process, where the process projects an image onto a resist. The method includes providing a lateral distribution of intensity values of the image at different depths with the resist. Next, the lateral position of an edge point of the feature is predicted in dependence upon a particular resist development time, and further in dependence upon the image intensity values at more than one depth within the resist.

Bulk image modeling for optical proximity correction

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.

Assist feature placement based on a focus-sensitive cost-covariance field

Growing community of inventors

Sunnyvale, CA, United States of America

Yongfa Fan