Semiconductor profile measurement based on a scanning conditional model
Methods and systems for measuring semiconductor structures based on a trained scanning conditional measurement model are described herein. A scanning conditional model is trained based on Design Of Experiments (DOE) measurement data associated with known values of one or more parameters of interest and a set of perturbed values of the one or more parameters of interest. The trained conditional model minimizes the output of an error function characterizing the error between the known values of the perturbed values of the one or more parameters of interest for the given DOE measurement data. During inference, an error value associated with each candidate value of one or more parameters of interest is determined by the trained scanning conditional measurement model. The estimated value of the parameter of interest is the candidate value of the parameter of interest associated with the minimum error value.
1 . A system comprising:
one or more metrology systems each including an illumination source and a detector, each illumination source configured to generate an amount of illumination light characterized by one or more physical properties and each detector configured to collect an amount of measurement data from measurements of one or more structures disposed on a first wafer, the amount of measurement data dependent on the one or more physical properties of the amount of illumination light and one or more physical characteristics of the one or more structures disposed on the first wafer; and
a computing system configured to:
generate a set of candidate values of one or more parameters of interest that characterize a shape of the one or more structures disposed on the first wafer;
determine an error value associated with each of the set of candidate values of the one or more parameters of interest based on a trained scanning conditional measurement model, wherein each of the set of candidate values and the amount of measurement data are provided as input to the trained scanning conditional measurement model;
determine a first estimated value of each of the one or more parameters of interest, wherein the first estimated value is a first of the set of candidate values of each of the one or more parameters of interest having a first minimum error value; and
communicate an indication of the first estimated value of each of the one or more parameters of interest to a semiconductor fabrication tool that causes the semiconductor fabrication tool to adjust one or more parameters of a fabrication process of the semiconductor fabrication tool to achieve a desired output from the semiconductor fabrication tool.
2 . The system of claim 1 , the computing system further configured to:
receive a Design Of Experiments (DOE) dataset of measurement data associated with measurements of a plurality of DOE structures characterized by a set of DOE parameter values; and
train the scanning conditional measurement model based on the DOE dataset of measurement data corresponding to the set of DOE parameter values and a set of perturbed values of each of the set of DOE parameter values.
3 . The system of claim 2 , wherein the DOE dataset of measurement data is actual measurement data collected from the plurality of DOE structures fabricated in accordance with the set of DOE parameter values.
4 . The system of claim 3 , wherein the set of DOE parameter values are measured by a trusted reference metrology system.
5 . The system of claim 3 , wherein the set of DOE parameter values are known, programmed values employed to fabricate the plurality of DOE structures.
6 . The system of claim 2 , wherein the DOE dataset of measurement data is simulated based on the set of DOE parameter values, wherein the set of DOE parameter values are known values associated with the simulation.
7 . The system of claim 2 , wherein the set of DOE parameter values are simulated based on a DOE dataset of process parameter values, and wherein the DOE dataset of measurement data is simulated based on the set of DOE parameter values.
8 . The system of claim 2 , wherein the scanning conditional measurement model is a machine learning based model.
9 . The system of claim 1 , the computing system further configured to:
determine a second estimated value of each of the one or more parameters of interest, wherein the second estimated value is a second of the set of candidate values of each of the one or more parameters of interest having a second minimum error value.
10 . The system of claim 1 , wherein the one or more parameters of interest characterize a two dimensional shape of the one or more structures disposed on the first wafer.
11 . The system of claim 1 , wherein the one or more parameters of interest characterize a three dimensional shape of the one or more structures disposed on the first wafer.
12 . The system of claim 1 , wherein the amount of measurement data includes measurements of the one or more structures by at least one optical based metrology system, at least one x-ray based metrology system, or any combination thereof.
13 . A method comprising:
collecting an amount of measurement data from measurements of one or more structures disposed on a first wafer, the collecting of the amount of measurement data involving an illumination source configured to generate an amount of illumination light characterized by one or more physical properties and a detector configured to collect the amount of measurement data, the amount of measurement data dependent on the one or more physical properties of the amount of illumination light and one or more physical characteristics of the one or more structures disposed on the first wafer;
generating a set of candidate values of one or more parameters of interest that characterize a shape of the one or more structures disposed on the first wafer;
determining an error value associated with each of the set of candidate values of the one or more parameters of interest based on a trained scanning conditional measurement model, wherein each of the set of candidate values and the amount of measurement data are provided as input to the trained scanning conditional measurement model;
determining a first estimated value of each of the one or more parameters of interest, wherein the first estimated value is a first of the set of candidate values of each of the one or more parameters of interest having a first minimum error value; and
communicating an indication of the first estimated value of each of the one or more parameters of interest to a semiconductor fabrication tool that causes the semiconductor fabrication tool to adjust one or more parameters of a fabrication process of the semiconductor fabrication tool to achieve a desired output from the semiconductor fabrication tool.
14 . The method of claim 13 , further comprising:
receiving a Design Of Experiments (DOE) dataset of measurement data associated with measurements of a plurality of DOE structures characterized by a set of DOE parameter values; and
training the scanning conditional measurement model based on the DOE dataset of measurement data corresponding to the set of DOE parameter values and a set of perturbed values of each of the set of DOE parameter values.
15 . The method of claim 14 , wherein the DOE dataset of measurement data is actual measurement data collected from the plurality of DOE structures fabricated in accordance with the set of DOE parameter values.
16 . The method of claim 14 , wherein the DOE dataset of measurement data is simulated based on the set of DOE parameter values, wherein the set of DOE parameter values are known values associated with the simulation.
17 . The method of claim 14 , wherein the set of DOE parameter values are simulated based on a DOE dataset of process parameter values, and wherein the DOE dataset of measurement data is simulated based on the set of DOE parameter values.
18 . The method of claim 14 , wherein the scanning conditional measurement model is a machine learning based model.
19 . The method of claim 13 , further comprising:
determining a second estimated value of each of the one or more parameters of interest, wherein the second estimated value is a second of the set of candidate values of each of the one or more parameters of interest having a second minimum error value.
20 . The method of claim 13 , wherein the one or more parameters of interest characterize a two dimensional shape or a three dimensional shape of the one or more structures disposed on the first wafer.
21 . The method of claim 13 , wherein the amount of measurement data includes measurements of the one or more structures by at least one optical based metrology system, at least one x-ray based metrology system, or any combination thereof.
22 . A system comprising:
one or more metrology systems each including an illumination source and a detector, each illumination source configured to generate an amount of illumination light characterized by one or more physical properties and each detector configured to collect an amount of measurement data from measurements of one or more structures disposed on a first wafer, the amount of measurement data dependent on the one or more physical properties of the amount of illumination light and one or more physical characteristics of the one or more structures disposed on the first wafer; and
a non-transitory, computer-readable medium including instructions that when executed by one or more processors of a computing system cause the computing system to:
generate a set of candidate values of one or more parameters of interest that characterize a shape of the one or more structures disposed on the first wafer;
determine an error value associated with each of the set of candidate values of the one or more parameters of interest based on a trained scanning conditional measurement model, wherein each of the set of candidate values and the amount of measurement data are provided as input to the trained scanning conditional measurement model;
determine a first estimated value of each of the one or more parameters of interest, wherein the first estimated value is a first of the set of candidate values of each of the one or more parameters of interest having a first minimum error value; and
communicate an indication of the first estimated value of each of the one or more parameters of interest to a semiconductor fabrication tool that causes the semiconductor fabrication tool to adjust one or more parameters of a fabrication process of the semiconductor fabrication tool to achieve a desired output from the semiconductor fabrication tool.
23 . The system of claim 22 , the non-transitory, computer-readable medium further including the instructions that when executed by the one or more processors of the computing system cause the computing system to:
receive a Design Of Experiments (DOE) dataset of measurement data associated with measurements of a plurality of DOE structures characterized by a set of DOE parameter values; and
train the scanning conditional measurement model based on the DOE dataset of measurement data corresponding to the set of DOE parameter values and a set of perturbed values of each of the set of DOE parameter values.