NEURAL NETS FOR DEFECT RECOGNITION ON MASKS AND INTEGRATED CIRCUITS : FIRST RESULTS. Hartmut Surmann, Benhür Kiziloglu, Ulrich Rückert & Karl Goser

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1 NEURAL NETS FOR DEFECT RECOGNITION ON MASKS AND INTEGRATED CIRCUITS : FIRST RESULTS Hartmut Surmann, Benhür Kiziloglu, Ulrich Rückert & Karl Goser University of Dortmund - Dortmund, Germany Abstract: The first results of applying art'rficial neural networks to defect detection on masks and integrated circuits will be presented. The use of neural nets opens up the possibility of developing rapid and problem-flexible inspection Systems. In this publication two different models of neural nets - the Kohonen map and a feedforward-net using the backpropagation learning algorithm - are discussed with regard to their applicability in pattern recognition for inspection Systems by means of Simulation results. Keywords : self-organising feature map, backpropagation, automatic inspection System, defect recognition. 581

2

3 Figure 1: Half-tone picture: 512x512 pixel, resolution 8 bit..d, No. 2 L >=lpn ^ Failure classesi Pinhole, Pinspot Pattern No.l, S, 7 Link-Defect Pattern Ho.6, 8 Edge-Defect Pattern Ho.2, 3 Corner-Defect Pattern Ho.4, 9 Significant data! nininal Structure Length: nininal detectable Error-Size: Resolution: L (l jjn) D= L/ Size of the Mask surface:

4 functionality of integrated circuits depends on this failures which can eff'ect cut-offs, short circuits and reduction or enlargement of circuit structures. Figure 2 also shows some significant data of the inspected mask: The minimal detectable failure size is 0.2 um, the minimal structure size is l (im. This leads

5

6 Concerning the number of layers our simulations have shown no remarkable

7 35 28 Error oc-o.7, I-O.8 NiMber 25 ZI

8 cation. For our indendet application (defect recognition on masks and integrated circuits) we have not succeeded in solving this Problem so far. Hence,

9 NetHorfc Topologu Net-KR hb, hl, bb, bl, nl, nj, Inputunltflnz B. S, 8. BZ, I.B, 1.98, 14, 14, 256 Nunber of Patterns 3«Learning Steps 38 Training T Ine s V Error Evaluation- Tine Net-KB B. 3, B. 92, B.B, l.bb, 14, 14, 2 SS»S 38 53»in 25 nln Ket-KB B.l, B. BZ, B. 8, 1.B8, IS, IS, ZS6 Z5S nln Table 7: Learning and inspection times ho, bo: learning step and radius size at the beginning

10 Figure 11: Result

11 Literature [1] Sischka, D., Bisek, R.: "Detection of Defects on the Surface of Micro-

12 -tas

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