PROCESS CONTROL TECHNIQUES FOR SEMICONDUCTOR MANUFACTURING PROCESSES

• US 20160148850A1
• Filed: 11/25/2015
• Published: 05/26/2016
• Est. Priority Date: 11/25/2014
• Status: Abandoned Application

• Current Assignee: Stream Mosaic, Inc.
• Original Assignee: Stream Mosaic, Inc.
• Inventors: DAVID, Jeffrey Drue
• Application Number:
  US14/952,266
• Publication Number:
  US 20160148850A1
• Time in Patent Office: Days
• US Class Current: 1/1
• 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: G03F 7/70625 Dimensions, e.g. line width, critical dimension [CD], profile, sidewall angle or edge roughness
• CPC Class Codes: G03F 7/70633 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 Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: H01L 21/67253 Process monitoring, e.g. flow or thickness monitoring
• 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 22/14 for electrical parameters, e.g. resistance, deep-levels, CV, diffusions by electrical means
• CPC Class Codes: H01L 22/20 Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps

Abstract

Techniques for measuring and/or compensating for process variations in a semiconductor manufacturing processes. Machine learning algorithms are used on extensive sets of input data, including upstream data, to organize and pre-process the input data, and to correlate the input data to specific features of interest. The correlations can then be used to make process adjustments. The techniques may be applied to any feature or step of the semiconductor manufacturing process, such as overlay, critical dimension, and yield prediction.

Claims

1. A method, comprising:

receiving real-time inputs of a current production run of semiconductor wafers from a lithography process and at least one upstream process into an overlay measurement model stored in a data processing apparatus, wherein the overlay measurement model is configured to determine a multi-variate relationship of a plurality of input data to overlay measurement, the input data is obtained from the lithography process and the upstream process in previous production runs;

generating a predicted overlay measurement from the real-time inputs using the overlay measurement model; and

adjusting the lithography process or the upstream process such that the predicted overlay measurement correlates with an actual overlay measurement.

2. The method of claim 1, further comprising:

the overlay measurement model obtains additional input data from processes in the previous production runs after the lithography process for use in determining the multi-variate relationship; and

feeding additional real-time inputs from processes after the lithography process into the model for each production run.

3. A method, comprising:

obtaining a plurality of overlay measurements from a plurality of wafers in a plurality of production runs of a lithography process, wherein each overlay measurement indicates an offset between a first set of features formed on a first layer and a second set of features formed on a second layer above the first layer;

collecting a set of input data from each production run including data obtained from the lithography process and data obtained from upstream processes;

analyzing the sets of input data to determine a multi-variate relationship of the input data to the overlay measurements;

generating a predicted overlay measurement for each set of input data; and

adjusting the lithography process or the upstream processes such that the predicted overlay measurements correlate with an actual overlay measurement.

4. The method of claim 3, further comprising:

creating a model for overlay measurement based on the analysis of the input data and the corresponding overlay measurements;

deploying the model for a wafer production run, wherein real-time inputs are obtained from the lithography process and the upstream processes and fed into the model;

generating a predicted overlay measurement using the model; and

adjusting the lithography process or the upstream processes such that the predicted overlay measurement correlates with an actual overlay measurement.

5. The method of claim 3, wherein the data obtained from the lithography process and the upstream processes includes metrology and parametric data.

6. The method of claim 5, wherein the metrology and parametric data from the lithography process includes feature critical dimensions, wafer shape, wafer geometry, film thickness, film resistivity, device channel length, device channel width, device channel depth, device operating thresholds, and device resistance.

7. The method of claim 5, wherein the metrology and parametric data from the upstream processes includes, for each upstream process, process duration, process temperature, process pressure, process frequency, and optical measurements.

8. The method of claim 3, wherein the overlay measurements are obtained using image-based overlay or diffraction-based overlay.

9. The method of claim 3, wherein the analyzing step is performed by at least one machine learning algorithm.

10. The method of claim 3, wherein the analyzing step is performed by a combination of machine learning algorithms.

11. The method of claim 3, wherein the analyzing step is performed by a multi-step algorithm.

12. The method of claim 4, further comprising:

creating a virtual metrology model based on the data obtained from upstream processes; and

providing an output of the virtual metrology model as an input to the overlay measurement model.

13. The method of claim 4, further comprising:

obtaining in-situ metrology data; and

providing the in-situ metrology data as an input to the overlay measurement model.

14. The method of claim 4, further comprising:

performing a transformation of one or more sets of the input data; and

providing the transformed input data as an input to the overlay measurement model.

15. The method of claim 4, further comprising:

normalizing the real-time inputs when a second statistical distribution of the real-time input has changed from a first statistical distribution of the input data

16. The method of claim 15, wherein the normalizing step is implemented by determining a z-score for the first and second statistical distributions.

17. A non-transitory machine-readable medium having stored thereon one or more sequences of instructions, which instructions, when executed by one or more processors, cause the one or more processors to carry out the steps of:

obtaining a plurality of overlay measurements from a plurality of wafers in a plurality of production runs of a lithography process, wherein each overlay measurement indicates an offset between a first set of features formed on a first layer and a second set of features formed on a second layer above the first layer;

collecting a set of input data from each production run including data obtained from the lithography process and data obtained from upstream processes;

analyzing the sets of input data to determine a multi-variate relationship of the input data to the overlay measurements;

generating a predicted overlay measurement for each set of input data; and

adjusting the lithography process or the upstream processes such that the predicted overlay measurements correlate with an actual overlay measurement.

18. The non-transitory machine-readable medium of claim 17, comprising further instructions that cause the one or more processors to carry out the steps of:

creating a model for overlay measurement based on the analysis of the input data and the corresponding overlay measurements;

deploying the model for a wafer production run, wherein real-time inputs are obtained from the lithography process and the upstream processes and fed into the model;

generating a predicted overlay measurement using the model; and

adjusting the lithography process or the upstream processes such that the predicted overlay measurement correlates with an actual overlay measurement.

19. A system, comprising:

at least one processor; and

a memory coupled to the processor comprising instructions executable by the processor, the instructions, when executed by the processor, cause the processor to;

obtain a plurality of overlay measurements from a plurality of wafers in a plurality of production runs of a lithography process, wherein each overlay measurement indicates an offset between a first set of features formed on a first layer and a second set of features formed on a second layer above the first layer;

collect a set of input data from each production run including data obtained from the lithography process and data obtained from upstream processes;

analyze the sets of input data to determine a multi-variate relationship of the input data to the overlay measurements;

generate a predicted overlay measurement for each set of input data; and

adjust the lithography process or the upstream processes such that the predicted overlay measurements correlate with an actual overlay measurement.

20. The system of claim 19, comprising further instructions that cause the processor to:

create a model for overlay measurement based on the analysis of the input data and the corresponding overlay measurements;

deploy the model for a wafer production run, wherein real-time inputs are obtained from the lithography process and the upstream processes and fed into the model;

generate a predicted overlay measurement using the model; and

adjust the lithography process or the upstream processes such that the predicted overlay measurement correlates with an actual overlay measurement.