Pattern selection for lithographic model calibration

• US 8,694,928 B2
• Filed: 11/05/2009
• Issued: 04/08/2014
• 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: Cao, Yu, Shao, Wenjin, Ye, Jun, Goossens, Ronaldus Johannes Gljsbertus
• Primary Examiner(s): Dinh, Paul
• Assistant Examiner(s): AISAKA, BRYCE M
• Application Number:
  US12/613,244
• Publication Number:
  US 20100122225A1
• Time in Patent Office: 1,615 Days
• Field of Search: 716/54
• US Class Current: 716/54
• 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 computer-implemented 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;

identifying a p-dimensional sensitivity space associated with the identified set of lithography model parameters, wherein each dimension in the p-dimensional sensitivity space corresponds to one parameter of the identified set of lithography model parameters, and wherein each dimension in the p-dimensional sensitivity space is substantially orthogonal to all other p−

1 dimensions in the p-dimensional sensitivity space;

calculating sensitivity variations along one or more dimensions in the p-dimensional sensitivity space associated with the set of test patterns; and

automatically selecting, by the computer, a set of test patterns from the identified pool of candidate test patterns that are most effective in determining optimal values of the identified set of lithography model parameters based on the calculated sensitivity variations associated with the set of test patterns.

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

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

performing calculations using the sensitivity matrix to determine which test pattern in the identified pool of candidate test patterns has the greatest sensitivity to an identified lithography 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 the selected set of test patterns are selected so that measured wafer data corresponding to the selected test patterns drives 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 according to claim 1, wherein automatically selecting includes:

identifying a group of the pool of the candidate test patterns having similar sensitivity to a given one of the set of lithography model parameters;

selecting a test pattern from among the group of the pool of the candidate test patterns based on the identification of the group so as to reduce unnecessary duplication of test patterns.

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

identifying a pool of candidate test patterns;

identifying a set of lithography model parameters;

identifying a p-dimensional sensitivity space associated with the identified set of lithography model parameters, wherein each dimension in the p-dimensional sensitivity space corresponds to one parameter of the identified set of lithography model parameters, and wherein each dimension in the p-dimensional sensitivity space is substantially orthogonal to all other p−

1 dimensions in the p-dimensional sensitivity space;

calculating sensitivity variations along one or more dimensions in the p-dimensional sensitivity space associated with the set of test patterns; and

automatically selecting, by the computer, a set of test patterns from the identified pool of candidate test patterns that are most effective in determining optimal values of the identified set of lithography model parameters based on the calculated sensitivity variations associated with the set of test patterns.