ON-DEVICE METROLOGY

• US 20140316730A1
• Filed: 04/14/2014
• Published: 10/23/2014
• Est. Priority Date: 04/19/2013
• Status: Active Grant

• Current Assignee: KLA-Tencor Corporation (KLA Corporation)
• Original Assignee: KLA-Tencor Corporation (KLA Corporation)
• Inventors: Shchegrov, Andrei V., Madsen, Jonathan M., Pandev, Stilian Ivanov, Levy, Ady, Kandel, Daniel, Adel, Michael E., Tadmor, Ori
• Granted Patent: US 9,875,946 B2
• Time in Patent Office: Days
• US Class Current: 702/81
• CPC Class Codes: H01L 22/12 for structural parameters, e.g. thickness, line width, refractive index, temperature, warp, bond strength, defects, optical inspection, electrical measurement of structural dimensions, metallurgic measurement of diffusions electrical measurement of diffusions H01L22/14
• CPC Class Codes: H01L 22/20 Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps
• CPC Class Codes: H01L 22/30 Structural arrangements specially adapted for testing or measuring during manufacture or treatment, or specially adapted for reliability measurements
• CPC Class Codes: H01L 2924/00 Indexing scheme for arrangements or methods for connecting or disconnecting semiconductor or solid-state bodies as covered by H01L24/00
• CPC Class Codes: H01L 2924/0002 Not covered by any one of groups H01L24/00, H01L24/00 and H01L2224/00

Abstract

Methods and systems for performing semiconductor metrology directly on device structures are presented. A measurement model is created based on measured training data collected from at least one device structure. The trained measurement model is used to calculate process parameter values, structure parameter values, or both, directly from measurement data collected from device structures of other wafers. In some examples, measurement data from multiple targets is collected for model building, training, and measurement. In some examples, the use of measurement data associated with multiple targets eliminates, or significantly reduces, the effect of under layers in the measurement result, and enables more accurate measurements. Measurement data collected for model building, training, and measurement may be derived from measurements performed by a combination of multiple, different measurement techniques.

Claims

1. A method comprising:

receiving a first amount of measurement data associated with measurements of a first plurality of sites on a surface of a first semiconductor wafer with known variations of at least one process parameter, structure parameter, or both, wherein at least one of the first plurality of sites includes a device structure;

determining an expected response model of each of the at least one known process parameters, structure parameters, or both;

determining an input-output measurement model based at least in part on the first amount of measurement data; and

training the input-output measurement model based on parameter values determined from the expected response model.

2. The method of claim 1, further comprising:

receiving a second amount of measurement data associated with measurements of a second plurality of sites on a surface of a second semiconductor wafer, wherein at least one of the second plurality of sites includes the device structure;

determining at least one process parameter value, at least one structural parameter value, or both, associated with each of the second plurality of sites based on a fitting of the second amount of measurement data to the trained input-output measurement model; and

storing any of the at least one process parameter value, the at least one structural parameter value, or both, in a memory.

3. The method of claim 1, wherein the first amount of measurement data is associated with measurements of a first plurality of sites with known variations of any of focus, exposure dosage, overlay, and critical dimension over the surface of the semiconductor wafer.

4. The method of claim 1, further comprising:

extracting one or more features of the first amount of measurement data by reducing a dimension of the first amount of measurement data, and wherein the determining the input-output measurement model is based at least in part on the one or more features.

5. The method of claim 4, wherein the reducing the dimension of the first amount of measurement data involves any of a principal components analysis, a non-linear principal components analysis, a selection of individual signals from the first amount of measurement data, and a filtering of the first amount of measurement data.

6. The method of claim 1, wherein the expected response model is a wafer map model, and wherein the determining the wafer map model involves fitting a two dimensional map function to the known process parameters, structure parameters, or both, associated with the first plurality of sites.

7. The method of claim 1, wherein the device structure is a dedicated on-device metrology structure.

8. The method of claim 7, wherein at least one of the first plurality of sites includes an assist structure located in a scribe line of the semiconductor wafer.

9. The method of claim 1, further comprising:

navigating to a measurement site on the surface of the semiconductor wafer based at least in part on one or more features extracted from an amount of measurement data.

10. The method of claim 9, wherein the navigating involves:

receiving an amount of measurement data associated with measurements of a location on a surface of a semiconductor wafer;

extracting one or more features from the amount of measurement data;

determining proximity to a desired measurement target on the surface of the semiconductor wafer based at least in part on a comparison of the one or more features with ideal features associated with the desired measurement target; and

adjusting the measurement location based on the proximity to the desired measurement target.

20. The method of claim 1, wherein the desired measurement target is an on-device structure.

11. A system comprising:

a metrology tool including an illumination source and a detector configured to perform measurements of a target structure; and

a computing system configured to;

receive a first amount of measurement data from the metrology tool, the first amount of data associated with measurements of a first plurality of sites on a surface of a first semiconductor wafer with known variations of at least one process parameter, structure parameter, or both, wherein at least one of the first plurality of sites includes a device structure;

determine an expected response model of each of the at least one known process parameters, structure parameters, or both;

determine an input-output measurement model based at least in part on the first amount of measurement data; and

train the input-output measurement model based on parameter values determined from the expected response model.

12. The system of claim 11, wherein the computing system is further configured to:

receive a second amount of measurement data associated with measurements of a second plurality of sites on a surface of a second semiconductor wafer;

determine at least one process parameter value, at least one structural parameter value, or both, associated with each of the second plurality of sites based on a fitting of the second amount of measurement data to the trained input-output measurement model; and

store any of the at least one process parameter value, the at least one structural parameter value, or both, in a memory.

13. The system of claim 11, wherein the computing system is further configured to:

extract one or more features of the first amount of measurement data by reducing a dimension of the first amount of measurement data, and wherein the determining the input-output measurement model is based at least in part on the one or more features.

14. The system of claim 13, wherein the reducing the dimension of the first amount of measurement data involves any of a principal components analysis, a non-linear principal components analysis, a selection of individual signals from the first amount of measurement data, and a filtering of the first amount of measurement data.

15. The system of claim 11, wherein the device structure is a dedicated on-device metrology structure.

16. The system of claim 15, wherein at least one of the first plurality of sites includes an assist structure located in a scribe line of the semiconductor wafer.

17. The system of claim 11, further comprising:

navigating to a measurement site on the surface of the semiconductor wafer based at least in part on one or more features extracted from an amount of measurement data.

18. A method comprising:

receiving a first amount of measurement data associated with measurements of a location on a surface of a semiconductor wafer;

extracting one or more features from the first amount of measurement data, wherein the extracting of the one or more features from the amount of measurement data involves a principal component analysis;

determining proximity to a desired measurement target on the surface of the semiconductor wafer based at least in part on a comparison of the one or more features with ideal features associated with the desired measurement target; and

adjusting the measurement location based on the proximity to the desired measurement target.

19. The method of claim 18, further comprising:

receiving a second amount of measurement data associated with measurements of a desired measurement target on the semiconductor wafer;

determining at least one process parameter value, at least one structural parameter value, or both, based on a fitting of the second amount of measurement data to a trained input-output measurement model; and

storing any of the at least one process parameter value, the at least one structural parameter value, or both, in a memory.