Analyzing root causes of process variation in scatterometry metrology

Patent metadata

Field	Value
Publication number	US-10203200-B2
Application number	US-201615329618-A
Country	US
Kind code	B2
Filing date	Nov 1, 2016
Priority date	Feb 25, 2016
Publication date	Feb 12, 2019
Grant date	Feb 12, 2019

Abstract

Method, metrology modules and RCA tool are provided, which use the behavior of resonance region(s) in measurement landscapes to evaluate and characterize process variation with respect to symmetric and asymmetric factors, and provide root cause analysis of the process variation with respect to process steps. Simulations of modeled stacks with different layer thicknesses and process variation factors may be used to enhance the analysis and provide improved target designs, improved algorithms and correctables for metrology measurements. Specific targets that exhibit sensitive resonance regions may be utilize to enhance the evaluation of process variation.

First claim

What is claimed is:

1. A method comprising:
deriving a landscape from simulation and/or from a plurality of scatterometry measurements of a semiconductor wafer using a metrology tool having a processor executing instructions on a computer readable storage medium, wherein the landscape comprises an at least partially continuous dependency of at least one metrology metric on at least one parameter,
identifying, in the derived landscape, at least one resonance region that corresponds to a resonance of optical illumination in a measured location using the processor,
analyzing a dependency of the identified at least one resonance region on specified changes in the at least one parameter using the processor, and
deriving from the analysis an estimation of process variation using the processor.

2. The method of claim 1 , wherein the at least one parameter comprises any of: an illumination wavelength, a pupil location, a measurement parameter, and a process parameter.

3. The method of claim 1 , wherein the at least one metrology metric comprises at least one of an overlay, an overlay variation metric and an inaccuracy metric defined with respect to the overlay.

4. The method of claim 3 , wherein the overlay is calculated as OVL({right arrow over (p)})=((D 1 ({right arrow over (p)})+D 2 ({right arrow over (p)}))/(D 1 ({right arrow over (p)})−D 2 ({right arrow over (p)})))·f 0 , with {right arrow over (p)} representing a pupil pixel, f 0 denoting a designed offset, and with D 1 and D 2 denoting, corresponding to opposite designed offsets, differences between signal intensities of opposing orders measured at pupil pixels which are rotated by 180° with respect to each other.

5. The method of claim 1 , further comprising detecting a movement of the at least one resonance region across a simulated and/or measured pupil image as the at least one parameter, and estimating symmetric process variation therefrom.

6. The method of claim 1 , further comprising detecting a sign change in the at least one resonance region, and estimating asymmetric process variation therefrom.

7. The method of claim 6 , wherein the sign change comprises a reversal of an inaccuracy gradient upon changing the at least one parameter.

8. The method of claim 5 , further comprising:
mapping the identified at least one resonance region over a plurality of measurement locations on a wafer,
characterizing at least one spatial relation in the mapping, and
identifying a root cause for the process variation according to the characterization.

9. The method of claim 8 , further comprising carrying out the root cause identification in parallel to metrology measurements to predict metrology excursions.

10. The method of claim 9 , further comprising adjusting a metrology measurements recipe according to the predicted metrology excursions.

11. The method of claim 1 , further comprising analyzing the derived landscape with respect to different sites on a wafer.

12. The method of claim 11 , further comprising using a reference wafer to derive the estimation of process variation in the sites with respect thereto.

13. The method of claim 11 , further comprising adjusting a sampling for a metrology measurements recipe to carry out the metrology measurements on sites across the wafer, which correspond to a same region of the landscape.

14. A computer program product comprising a non-transitory computer readable storage medium having computer readable program embodied therewith, the computer readable program configured to carry out the method of claim 1 .

15. A metrology tool configured to carry out the method of claim 1 .

16. An RCA (root cause analysis) tool associated with a metrology tool that includes at least one computer processor, the RCA tool being configured to receive process related parameters and measurement data of a semiconductor wafer, and derive therefrom a landscape and at least one resonance region in the landscape, wherein the landscape comprises a dependency of at least one metrology metric on at least one parameter and the at least one resonance region corresponds to a resonance of optical illumination in a measured location, the RCA tool being further configured to evaluate a process variation from an analysis of changes in the at least one resonance region in the landscape with respect to the received process related parameters and measurement data.

17. The RCA tool of claim 16 , further configured to map changes in the at least one resonance region over at least one wafer.

18. The RCA tool of claim 17 , wherein the mapping is carried out with respect to at least one of: a wafer, a process-split wafer, and a plurality of wafers of a lot.

19. The RCA tool of claim 16 , further configured to provide correctables based on the evaluated process variation.

20. The RCA tool of claim 16 , further configured to derive a root cause analysis of evaluated process variation.

21. The RCA tool of claim 20 , further configured to simulate a modeled stack with various homogenous thickness variations to derive patterns of process variation, and to relate the derived patterns to specific process-related root causes.

22. The RCA tool of claim 21 , further configured to incorporate in the simulation asymmetric process variation factors comprising at least one of: grating asymmetry, topography, cell-to-cell variation, target noise, and any process variation that breaks the asymmetry inside the modeled stack.

23. The RCA tool of claim 22 , further configured to quantify an expected inaccuracy for different types of process variations and suggest modifications to at least one of: target designs, metrology algorithms and measurement recipes.

Assignees

• Kla Tencor Corp

Inventors

• Marciano Tal
• Adel Michael E
• Ghinovker Mark
• Bringoltz Barak
• Klein Dana
• Itzkovich Tal
• Ramanathan Vidya
• Camp Janay

Classifications

• H10P74/203

  Structural properties, e.g. testing or measuring thicknesses, line widths, warpage, bond strengths or physical defects · CPC title

• H10P74/23

  characterised by multiple measurements, corrections, marking or sorting processes · CPC title

• G03F7/70633

  Overlay, i.e. relative alignment between patterns printed by separate exposures in different layers, or in the same layer in multiple exposures or stitching · CPC title

• G03F7/70616 Primary

  Monitoring the printed patterns · CPC title

• G01B11/272 Primary

  using photoelectric detection means · CPC title