Deep Learning Based Method for Automatic Focus Detection in Digital Lithography

Table 1. Accuracy of network models with different layer structures on the test set
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Table 1. Accuracy of network models with different layer structures on the test set
Network model Accuracy/%
ResNet18 70.0
ResNet34 77.5
ResNet50 77.5

Table 2. Accuracy of different layer structured network models with added feature fusion modules on the test set
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Table 2. Accuracy of different layer structured network models with added feature fusion modules on the test set
Network model Accuracy/%
ResNet18+FF 77.5
ResNet34+FF 83.1
ResNet50+FF 84.5

Table 3. Time required to process images with different definition evaluation functions
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Table 3. Time required to process images with different definition evaluation functions
Image definition evaluation function Time taken to evaluate a single image /s
Laplacian 0.005
Energy 0.998
Brenner 0.247
SMD 0.991
variance 0.771

Table 4. Focus detection performance of the same pattern in different out-of-focus situations using the proposed method

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Table 4. Focus detection performance of the same pattern in different out-of-focus situations using the proposed method

Off-focus volume/μm	Coarse check focus results	Steps used for precision focus checks	Total time/ms	Focus check results
-40	Grade 4 negative defocus 98%	6	263.8	True
-29	Grade 3 negative defocus 53.8%	5	238.4	True
-18	Grade 2 negative defocus 88%	4	212.1	True
-10	Grade 1 negative defocus 58.4%	3	191.4	True
-1	Coarse focal plane 80%	2	168.9	True
9	Grade 1 positive defocus 54.8%	3	188.8	True
17	Grade 2 positive defocus 78.6%	4	212.9	True
28	Grade 3 positive defocus 73.5%	5	241.1	True
39	Grade 4 positive defocus 96.8%	6	263.5	True

Table 5. Focus detection performance of the same pattern in different out-of-focus situations using conventional methods

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Table 5. Focus detection performance of the same pattern in different out-of-focus situations using conventional methods

Off-focus volume /μm	Steps used for focus checks	Total time/ms	Focus check results
-40	41	1 023.1	True
-29	30	729.3	True
-18	19	478.1	True
-10	11	302.4	True
-1	2	75.6	True
9	10	298.1	True
17	18	520.5	True
28	29	804.1	True
39	40	1 055.2	True

Table 6. Comparison of focus detection performance of different patterns at 28 μm out of focus

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Table 6. Comparison of focus detection performance of different patterns at 28 μm out of focus

Coarse check focus results	Time for coarse focus checks /ms	Steps used for precision focus checks	Total time for precision focus checks /ms
Grade 3 positive defocus78.6%	93.7	5	148.6
Grade 3 positive defocus73.5%	93.7	5	145.5
Grade 3 positive defocus61.8%	93.7	5	147.2

Table 7. Comparison of focus detection performance of different patterns at -18 μm out of focus

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Table 7. Comparison of focus detection performance of different patterns at -18 μm out of focus

Coarse check focus results	Time for coarse focus checks/ms	Steps used for precision focus checks	Total time for precision focus checks/ms
Grade 2 negative defocus68.4%	93.7	4	123.6
Grade 2 negative defocus88%	93.7	4	123.3
Grade 2 negative defocus76%	93.7	4	125.6

Table 8. Results of the coarse and precise focus detection errors in 20 tests
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Table 8. Results of the coarse and precise focus detection errors in 20 tests
Focal plane detection error/μm Number of times each error occurs in the coarse check focus Number of times each error occurs in the precision check focus
0 1 2
±1 3 15
±2 4 3
±3 3 0
±4 2 0
±5 3 0
±6 1 0
±7 3 0

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Jupu YANG, Jialin DU, Fanxing LI, Qingrong CHEN, Simo WANG, Wei YAN. Deep Learning Based Method for Automatic Focus Detection in Digital Lithography[J]. Acta Photonica Sinica, 2022, 51(6): 0611002

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Paper Information

Category:

Received: Jan. 6, 2022

Accepted: Feb. 23, 2022

Published Online: Sep. 23, 2022

The Author Email: YAN Wei (yanwei@ioe.ac.cn)

DOI:10.3788/gzxb20225106.0611002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Accuracy of network models with different layer structures on the test set

Table 1. Accuracy of network models with different layer structures on the test set

Table 2. Accuracy of different layer structured network models with added feature fusion modules on the test set

Table 2. Accuracy of different layer structured network models with added feature fusion modules on the test set

Table 3. Time required to process images with different definition evaluation functions

Table 3. Time required to process images with different definition evaluation functions

Table 4. Focus detection performance of the same pattern in different out-of-focus situations using the proposed method

Table 4. Focus detection performance of the same pattern in different out-of-focus situations using the proposed method

Table 5. Focus detection performance of the same pattern in different out-of-focus situations using conventional methods

Table 5. Focus detection performance of the same pattern in different out-of-focus situations using conventional methods

Table 6. Comparison of focus detection performance of different patterns at 28 μm out of focus

Table 6. Comparison of focus detection performance of different patterns at 28 μm out of focus

Table 7. Comparison of focus detection performance of different patterns at -18 μm out of focus

Table 7. Comparison of focus detection performance of different patterns at -18 μm out of focus

Table 8. Results of the coarse and precise focus detection errors in 20 tests

Table 8. Results of the coarse and precise focus detection errors in 20 tests

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