Binarization method and freeform mask optimization flow

Patent metadata

Field	Value
Publication number	US-2020363713-A1
Application number	US-201916967789-A
Country	US
Kind code	A1
Filing date	Feb 15, 2019
Priority date	Feb 18, 2018
Publication date	Nov 19, 2020
Grant date	—

Abstract

A method to determine a mask pattern for a patterning device. The method includes obtaining a target pattern to be printed on a substrate, an initial continuous tone image corresponding to the target pattern, a binarization function (e.g., a sigmoid, an arctan, a step function, etc.) configured to transform the initial continuous tone image, and a process model configured to predict a pattern on the substrate from an output of the binarization function; and generating a binarized image having a mask pattern corresponding to the initial continuous tone image by iteratively updating the initial continuous tone image based on a cost function such that the cost function is reduced. The cost function (e.g., EPE) determines a difference between a predicted pattern determined by the process model and the target pattern.

First claim

1 . A method comprising:
obtaining (i) a target pattern to be printed on a substrate subjected to a patterning process, (ii) an initial continuous tone image corresponding to the target pattern, (iii) a binarization function configured to transform the initial continuous tone image, and (iv) a process model configured to predict a pattern on the substrate from an output of the binarization function; and generating, by a hardware computer system, a binarized image having a mask pattern for a patterning device and corresponding to the initial continuous tone image by iteratively updating the initial continuous tone image based on a cost function such that the cost function is reduced, wherein the cost function determines a difference between a predicted pattern determined by the process model and the target pattern.

2 . The method of claim 1 , wherein the binarization function is a sigmoid function, an arctan function, and/or a step function.

3 . The method of claim 1 , wherein the mask pattern is a curvilinear pattern and/or a Manhattan pattern.

4 . The method of claim 1 , wherein an iteration of determining the curvilinear pattern comprises:
generating a transformed image by applying a binarization function to the initial continuous tone image; predicting, via simulation of the process model, a pattern from the transformed image; determining a gradient of the cost function; and modifying a value of one or more mask variables corresponding to the initial continuous tone image and/or one or more parameters of the binarization function based on the gradient of the cost function such that the cost function is reduced.

5 . The method of claim 4 , wherein the determining the gradient of the cost function involves computing a complete gradient over mask variables for the binarization function.

6 . The method of claim 4 , wherein modifying a value of one or more mask variables and/or one or more parameters of the binarization function comprises:
applying an optimization process to the gradient of the cost function; and identifying a value of one or more the mask variables and/or one or more parameters of the binarization function that result in a minimum gradient value.

7 . The method of claim 6 , comprising modifying a value of one or more mask variables and wherein the one or more mask variables are intensity values of pixels within the initial continuous tone image.

8 . The method of claim 6 , comprising modifying one or more parameters of the binarization function and wherein the one or more parameters of the binarization function comprise a steepness and threshold.

9 . The method of claim 1 , wherein the cost function is minimized.

10 . The method of claim 1 , wherein the cost function is an edge placement error and/or a mask rule check violation probability.

11 . The method of claim 1 , wherein the initial continuous tone image is a continuous transmission mask image comprising features corresponding to the target pattern and sub-resolution assist features.

12 . The method of claim 1 , further comprising manufacturing a patterning device including structural features corresponding to the binarized image.

13 . The method of claim 1 , further comprising performing, by a lithographic apparatus, a patterning step using a patterning device having structural features corresponding to the binarized image to print a corresponding pattern on the substrate.

14 . The method of claim 12 , wherein the structural features correspond to optical proximity corrections including assist features and/or contour modification.

15 . A computer program product comprising a non-transitory computer readable medium having instructions therein, the instructions, when executed by a computer, configured to cause the computer to at least:
obtain (i) a target pattern to be printed on a substrate subjected to a patterning process, (ii) an initial continuous tone image corresponding to the target pattern, (iii) a binarization function configured to transform the initial continuous tone image, and (iv) a process model configured to predict a pattern on the substrate from an output of the binarization function; and generate a binarized image having a mask pattern for a patterning device and corresponding to the initial continuous tone image by iterative updating of the initial continuous tone image based on a cost function such that the cost function is reduced, wherein the cost function determines a difference between a predicted pattern determined by the process model and the target pattern.

16 . The computer program product of claim 15 , wherein the binarization function is a sigmoid function, an arctan function, and/or a step function.

17 . The computer program product of claim 15 , wherein an iteration of determination of the curvilinear pattern comprises:
generation of a transformed image by applying a binarization function to the initial continuous tone image; prediction, via simulation of the process model, of a pattern from the transformed image; determination of a gradient of the cost function; and modification of a value of one or more mask variables corresponding to the initial continuous tone image and/or one or more parameters of the binarization function based on the gradient of the cost function such that the cost function is reduced.

18 . The computer program product of claim 15 , wherein the cost function is an edge placement error and/or a mask rule check violation probability.

19 . The computer program product of claim 15 , wherein the initial continuous tone image is a continuous transmission mask image comprising features corresponding to the target pattern and sub-resolution assist features.

20 . The computer program product of claim 15 , wherein the instructions are further configured to cause the computer to output information for manufacturing a patterning device including structural features corresponding to the binarized image.

Assignees

• Asml Netherlands Bv

Inventors

• Hsu Duan-Fu Stephen
• Liu Jingjing
• Howell Rafael C
• Peng Xingyue

Classifications

• G03F1/70 Primary

  Adapting basic layout or design of masks to lithographic process requirements, e.g., second iteration correction of mask patterns for imaging · CPC title

• G03F1/36 Primary

  Masks having proximity correction features; Preparation thereof, e.g. optical proximity correction [OPC] design processes · CPC title

• Y02P90/02

  Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS] · CPC title

• G06F30/398

  Design verification or optimisation, e.g. using design rule check [DRC], layout versus schematics [LVS] or finite element methods [FEM] (optical proximity correction [OPC] design processes G03F1/36) · CPC title

• G06F2119/18

  Manufacturability analysis or optimisation for manufacturability · CPC title