PATTERN SELECTION FOR LITHOGRAPHIC MODEL CALIBRATION

• US 20100122225A1
• Filed: 11/05/2009
• Published: 05/13/2010
• Est. Priority Date: 11/10/2008
• Status: Active Grant

• Current Assignee: ASML Netherlands BV (ASML Holding NV)
• Original Assignee: ASML Netherlands BV (ASML Holding NV)
• Inventors: Goossens, Ronaldus Johannes Gljsbertus, Ye, Jun, Cao, Yu, Shao, Wenjin
• Granted Patent: US 8,694,928 B2
• Time in Patent Office: Days
• US Class Current: 716/136
• CPC Class Codes: G03F 1/44 Testing or measuring features, e.g. grid patterns, focus monitors, sawtooth scales or notched scales
• CPC Class Codes: G03F 1/62 Pellicles, e.g. pellicle assemblies, e.g. having membrane on support frame; Preparation thereof
• CPC Class Codes: G03F 1/68 Preparation processes not covered by groups G03F1/20 - G03F1/50
• CPC Class Codes: G03F 7/70433 Layout for increasing efficiency or for compensating imaging errors, e.g. layout of exposure fields for reducing focus errors; Use of mask features for increasing efficiency or for compensating imaging errors
• CPC Class Codes: G03F 7/705 Modelling or simulating from physical phenomena up to complete wafer processes or whole workflow in wafer productions
• CPC Class Codes: G06F 17/10 Complex mathematical operations function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544
• CPC Class Codes: G06F 30/00 Computer-aided design [CAD]
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30

Abstract

The present invention relates generally to methods and apparatuses for test pattern selection for computational lithography model calibration. According to some aspects, the pattern selection algorithms of the present invention can be applied to any existing pool of candidate test patterns. According to some aspects, the present invention automatically selects those test patterns that are most effective in determining the optimal model parameter values from an existing pool of candidate test patterns, as opposed to designing optimal patterns. According to additional aspects, the selected set of test patterns according to the invention is able to excite all the known physics and chemistry in the model formulation, making sure that the wafer data for the test patterns can drive the model calibration to the optimal parameter values that realize the upper bound of prediction accuracy imposed by the model formulation.

Claims

1. A method of test pattern selection for computational lithography model calibration, comprising:

identifying a pool of candidate test patterns;

identifying a set of lithography model parameters; and

automatically selecting a set of test patterns from the pool of candidate test patterns that are most effective in determining optimal values of the identified model parameters.

2. A method according to claim 1, wherein the automatically selecting step includes:

preparing a sensitivity matrix corresponding to the identified pool of test patterns and model parameters;

performing calculations using the sensitivity matrix to determine which one of the identified test patterns has the greatest sensitivity to an identified model parameter; and

adding the determined test pattern to the selected set of test patterns.

3. A method according to claim 2, further comprising repeating the performing and adding steps until test patterns corresponding to all of the identified model parameters have been added to the selected set of test patterns.

4. A method according to claim 2, wherein the step of performing calculations includes performing principal component analysis on the sensitivity matrix.

5. A method according to claim 2, wherein the step of preparing the sensitivity matrix includes estimating wafer measurement uncertainties based on a simulated aerial image corresponding to the identified pool of test patterns.

6. A method according to claim 2, wherein the step of preparing the sensitivity matrix includes simulating an aerial image based on a mask including the identified pool of test patterns and using nominal values of certain of the identified model parameters.

7. A method according to claim 1, wherein the automatically selecting step includes minimizing a cost function related to model accuracy.

8. A method according to claim 7, wherein the cost function represents CD prediction errors for a certain pool of patterns, including a full chip pattern set.

9. A method according to claim 1, wherein the selected set of test patterns are selected to excite known physics and chemistry in a formulation of the lithography model.

10. A method according to claim 9, wherein measured wafer data corresponding to the selected test patterns can be used to drive the model calibration to the optimal parameter values so as to realize an upper bound of prediction accuracy imposed by a formulation of the lithography model.

11. A method of test pattern selection for computational lithography model calibration, comprising:

preparing a sensitivity matrix corresponding to a pool of candidate test patterns and a set of model parameters;

performing calculations using the sensitivity matrix; and

selecting a set of test patterns from the pool of candidate test patterns based on the calculations.

12. A method according to claim 11, wherein the selecting step includes:

identifying which of the pool of candidate test patterns has the greatest sensitivity to certain of the set of model parameters; and

adding the identified candidate test pattern to the set of test patterns.

13. A method according to claim 12, further comprising repeating the performing, identifying and adding steps until test patterns corresponding to all of the set of model parameters have been added to the selected set of test patterns.

14. A method according to claim 11, wherein the step of performing calculations includes:

performing principal component analysis on the sensitivity matrix.

15. A method of test pattern selection for computational lithography model calibration, comprising:

developing a sensitivity matrix that corresponds to a cost function associated with accuracy of a lithography model, wherein the sensitivity matrix establishes a relationship between a pool of candidate test patterns and a set of parameters of the lithography model; and

selecting a set of test patterns from the pool of candidate test patterns by performing calculations on the sensitivity matrix.

16. A method according to claim 15, wherein the selecting step includes:

determining which one of the identified test patterns has the greatest sensitivity to an identified model parameter using results of the calculations; and

adding the determined test pattern to the selected set of test patterns.

17. A method according to claim 16, further comprising repeating the performing, determining and adding steps until test patterns corresponding to all of the set of model parameters have been added to the set of test patterns.

18. A method according to claim 15, wherein the step of performing calculations includes performing principal component analysis on the sensitivity matrix.

19. A method according to claim 15, selecting step is performed so as to minimize the cost function.

20. A method according to claim 15, wherein the cost function represents CD prediction errors for a certain pool of patterns, including a full chip pattern set.

21. A computer program product comprising one or more computer-readable storage media having computer-executable instructions for causing a computer to select test patterns for calibrating a computational lithography model, the instructions causing the computer to perform a method comprising:

identifying a pool of candidate test patterns;

identifying a set of lithography model parameters; and

automatically selecting a set of test patterns from the pool of candidate test patterns that are most effective in determining optimal values of the identified model parameters.