Process-metrology reproducibility bands for lithographic photomasks

Patent metadata

Field	Value
Publication number	US-10210292-B2
Application number	US-201715838423-A
Country	US
Kind code	B2
Filing date	Dec 12, 2017
Priority date	Mar 26, 2015
Publication date	Feb 19, 2019
Grant date	Feb 19, 2019

Abstract

A photomask lithography simulation model is created for making a semiconductor chip. Poor metrology is filtered and removed from a contour-specific metrology dataset to improve performance of the photomask. Filtering is performed by the application of a weighting scheme.

First claim

What is claimed is:

1. A method comprising:
determining an average contour based on a filtered subset of a plurality of scanning electron microscopy (SEM) metrology datasets corresponding to a target contour;
computing an image parameter for a set of gauges for the target contour;
correlating the image parameter and a process-metrology reproducibility (PMR) band to generate a parameter to PMR band correlation;
determining a sampling count for the target contour based at least in part on the parameter to PMR band correlation;
computing an image log-scope (ILS) value for each gauge in the set of gauges;
generating a weight function for the target contour based at least in part on a PMR variance and the ILS value;
creating a lithography simulation model based on the weight function;
calibrating a photoresist compact model according to the lithography simulation model;
generating photomask shapes with the photoresist compact model; and
creating an integrated circuit based on the photomask shapes;
wherein:
the filtered subset excludes unphysical excursions of the target contour.

2. The method of claim 1 , further comprising:
assigning an increased relative weight for a selected contour;
wherein the selected contour is selected from a set of target contours based on specified outer limits of the PMR band and the parameter to PMR band correlation.

3. The method of claim 1 , further comprising;
extracting the target contour from a set of SEM images.

4. The method of claim 1 , further comprising:
identifying, based on a fragmentation setting, the set of gauges for the target contour.

5. The method of claim 1 , wherein:
the step of correlating the image parameter and a PMR band includes placing a set of measurement markers according to one of a first fragmentation setting where markers are placed for a two-dimensional target contour, and a second fragmentation setting where markers are placed for a one-dimensional target contour; and
the first fragmentation setting applies a more dense set of markers than the second fragmentation setting.

6. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a computer to cause the computer to:
determine an average contour based on a filtered subset of a plurality of scanning electron microscopy (SEM) metrology datasets corresponding to a target contour;
compute an image parameter for a set of gauges for the target contour;
correlate the image parameter and a process-metrology reproducibility (PMR) band to generate a parameter to PMR band correlation;
determine a sampling count for the target contour based at least in part on the parameter to PMR band correlation;
compute an image log-scope (ILS) value for each gauge in the set of gauges;
generate a weight function for the target contour based at least in part on a PMR variance and the ILS value;
create a lithography simulation model based on the weight function;
calibrate a photoresist compact model according to the lithography simulation model;
generate photomask shapes with the photoresist compact model; and
create an integrated circuit based on the photomask shapes;
wherein:
the filtered subset excludes unphysical excursions of the target contour.

7. The computer program product of claim 6 , further comprising:
program instructions readable by a computer to cause the computer to assign an increased relative weight for a selected contour;
wherein:
the selected contour is selected from a set of target contours based on specified outer limits of the PMR band and the parameter to PMR band correlation.

8. The computer program product of claim 6 , further comprising:
program instructions readable by a computer to cause the computer to extract the target contour from a set of SEM images.

9. The computer program product of claim 6 , further comprising:
program instructions readable by a computer to cause the computer to identify the set of gauges for the target contour.

10. The computer program product of claim 6 , wherein:
the program instructions readable by a computer to cause the computer to correlate the image parameter and the PMR band includes causing the computer to place a set of measurement markers according to one of a first fragmentation setting where markers are placed for a two-dimensional target contour and a second fragmentation setting where markers are placed for a one-dimensional target contour; and
the first fragmentation setting applies a more dense set of markers than the second fragmentation setting.

11. A computer system comprising:
a processor set; and
a computer readable storage medium;
wherein:
the processor set is structured, located, connected, and/or programmed to run program instructions stored on the computer readable storage medium; and
the program instructions include:
a first set of program instructions programmed to determine an average contour based on a filtered subset of a plurality of scanning electron microscopy (SEM) metrology datasets corresponding to a target contour;
a second set of program instructions programmed to compute an image parameter for a set of gauges for the target contour;
a third set of program instructions programmed to correlate the image parameter and a process-metrology reproducibility (PMR) band to generate a parameter to PMR band correlation;
a fourth set of program instructions programmed to determine a sampling count for the target contour based at least in part on the parameter to PMR band correlation;
a fifth set of program instructions programmed to compute an image log-scope (ILS) value for each gauge in the set of gauges;
a sixth set of program instructions programmed to generate a weight function for the target contour based at least in part on a PMR variance and the ILS value;
a seventh set of program instructions programmed to create a lithography simulation model based on the weight function; and
an eighth set of program instructions programmed to calibrate a photoresist compact model according to the lithography simulation model;
a ninth set of program instructions programmed to generate photomask shapes with the photoresist compact model; and
a tenth set of program instructions programmed to create an integrated circuit based on the photomask shapes;
wherein:
the filtered subset excludes unphysical excursions of the target contour.

12. The computer system of claim 11 , further comprising:
an eleventh set of program instructions programmed to assign an increased relative weight for a selected contour;
wherein:
the selected contour is selected from a set of target contours based on specified outer limits of the PMR band and the parameter to PMR band correlation.

13. The computer system of claim 11 , further comprising:
an eleventh set of program instructions programmed to extract the target contour from a set of SEM images.

14. The computer system of claim 11 , further comprising:
an eleventh set of program instructions programmed to identify the set of gauges for the target contour.

15. The computer system of claim 11 , wherein:
the third set of program instructions programmed to correlate the image parameter and the PMR band to includes causing the computer to place a set of measurement markers according to one of a first fragmentation setting where markers are placed for a two-dimensional target contour and a second fragmentation setting where markers are placed for a one-dimensional target contour; and
the first fragmentation setting applies a more dense set of

Assignees

• IBM

Inventors

• Bailey Todd C
• Graur Ioana C
• Halle Scott D
• Miller Marshal A

Classifications

• G06F30/20 Primary

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G03F7/70616 Primary

  Monitoring the printed patterns · 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

• G03F7/70625

  Dimensions, e.g. line width, critical dimension [CD], profile, sidewall angle or edge roughness · CPC title

• G06F17/5009 Primary

  Physics · mapped topic