Optimizing High-Throughput SEM for Large-area Defect Characterization in AM Steel
Contents
Supplementary Information
Table SI.1:Overview of algroithm detection Parameters used for raw images
Pixel Size [nm] | Dwell time [µs] | Gaussian Fidelity | Gaussian Range | Canny Sigma | K size | Canny Minimum | Canny Maximum |
---|---|---|---|---|---|---|---|
48.8 | 1 | 30 | 351 | 3.10 | 19 | 7 | 41 |
0.75 | 40 | 351 | 3.10 | 19 | 7 | 41 | |
0.5 | 40 | 351 | 3.10 | 19 | 7 | 41 | |
0.3 | 20 | 191 | 10.00 | 25 | 5 | 20 | |
0.1 | 20 | 191 | 10.00 | 25 | 5 | 20 | |
97.6 | 5 | 25 | 151 | 2.10 | 13 | 77 | 126 |
1 | 30 | 111 | 2.10 | 13 | 57 | 106 | |
0.75 | 61 | 425 | 33.90 | 5 | 137 | 87 | |
0.5 | 35 | 151 | 2.10 | 13 | 40 | 106 | |
0.3 | 40 | 191 | 13.00 | 13 | 15 | 50 | |
0.1 | 40 | 191 | 15.00 | 15 | 20 | 50 | |
195.3 | 10 | 30 | 111 | 2.10 | 13 | 67 | 106 |
5 | 42 | 421 | 1.40 | 115 | 144 | 98 | |
1 | 41 | 305 | 34.50 | 65 | 141 | 215 | |
0.75 | 42 | 421 | 1.40 | 115 | 144 | 98 | |
0.5 | 51 | 257 | 72.40 | 69 | 225 | 91 | |
0.3 | 42 | 421 | 1.40 | 115 | 144 | 98 | |
0.1 | 40 | 191 | 3.20 | 21 | 20 | 50 | |
390.6 | 10 | 33 | 153 | 11.06 | 31 | 33 | 212 |
5 | 37 | 153 | 9.08 | 43 | 0 | 252 | |
1 | 37 | 197 | 33.71 | 31 | 138 | 125 | |
0.75 | 47 | 119 | 34.46 | 51 | 73 | 178 | |
0.5 | 51 | 189 | 50.00 | 51 | 0 | 163 | |
0.3 | 23 | 117 | 43.72 | 5 | 150 | 165 | |
0.1 | 45 | 209 | 11.50 | 61 | 14 | 184 |
Table SI.2:Overview of algroithm detection Parameters used for denoised images
Pixel Size [nm] | Dwell time [µs] | Gaussian Fidelity | Gaussian Range | Canny Sigma | K size | Canny Minimum | Canny Maximum |
---|---|---|---|---|---|---|---|
48.8 | 1 | 17 | 503 | 2.41 | 15 | 167 | 71 |
0.75 | 10 | 561 | 27.45 | 71 | 119 | 219 | |
0.5 | 13 | 515 | 50.00 | 51 | 180 | 255 | |
0.3 | 7 | 513 | 36.30 | 45 | 110 | 225 | |
0.1 | 7 | 361 | 9.28 | 39 | 99 | 249 | |
97.6 | 5 | 16 | 219 | 9.47 | 51 | 219 | 211 |
1 | 12 | 211 | 6.78 | 45 | 58 | 27 | |
0.75 | 10 | 319 | 12.22 | 51 | 11 | 119 | |
0.5 | 10 | 277 | 24.13 | 51 | 9 | 248 | |
0.3 | 10 | 321 | 0.10 | 51 | 0 | 255 | |
0.1 | 9 | 317 | 1.56 | 51 | 95 | 120 | |
195.3 | 10 | 14 | 127 | 3.10 | 9 | 50 | 102 |
5 | 13 | 109 | 7.23 | 35 | 14 | 184 | |
1 | 14 | 99 | 0.10 | 27 | 127 | 169 | |
0.75 | 13 | 109 | 0.10 | 35 | 22 | 255 | |
0.5 | 13 | 109 | 0.10 | 51 | 0 | 255 | |
0.3 | 10 | 107 | 0.10 | 3 | 18 | 203 | |
0.1 | 11 | 123 | 23.07 | 71 | 45 | 255 | |
390.6 | 10 | 11 | 65 | 20.21 | 71 | 227 | 229 |
5 | 9 | 109 | 2.76 | 35 | 123 | 132 | |
1 | 10 | 73 | 50.00 | 19 | 255 | 199 | |
0.75 | 15 | 79 | 45.39 | 3 | 251 | 164 | |
0.5 | 3 | 3 | 0.49 | 39 | 0 | 134 | |
0.3 | 16 | 151 | 24.43 | 49 | 204 | 196 | |
0.1 | 20 | 143 | 2.02 | 25 | 86 | 41 |