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edfas.org 31 ELECTRONIC DEV ICE FA I LURE ANALYSIS | VOLUME 23 NO . 3 SUMMARY AND CONCLUSION Photon emission is a key technique for IC analysis in many applications. Recent work showed that time- resolved detection methods and especially the single- point scanner-based ones can bring valuable capabilities to the study of the latest devices. However, it is challenged by various issues suchas optical resolution, reduced image resolution for signal optimization, complex signals to interpret or low SNR. This article described a process based on a statistical approach of photon emission. Instead of directly gener- ating images from photon counting, we first suggest to identify the distributionof photons generatedby the same group of transistors and then generate a new image com- posed of the probability density functions associatedwith each of these distributionswith a higher resolution. Use of unsupervised learning algorithms such as ST-DBSCAN is a key step in this process to identify the groups of photons. This process can also identify fainter emission sources. It nonetheless remains at its early development stage and much more research work is needed before it constitutes a robust solution to the various challenges met in photon emission and, more broadly, IC analysis. REFERENCES 1. C. Boit, A. Beyreuther, and N. Herfurth: “Photon Emission in Silicon Based Devices,” Microelectronics Failure Analysis Desk Reference, 7th Edition, 2019, ASM International, p. 180-195. 2. R. Gopinath, et al.: “Dynamic Photon Emission on FinFET Devices Through Novel Scan Test Approaches,” Proc. Int. Symp. Test. Fail. Anal. (ISTFA), 2019, p. 160-163. 3. F. Stellari, et al.: “Resistive Random Access Memory Filament Visualization and Characterization Using Photon Emission Microscopy,” IEEEElectronDevice Letters, 42 (6), June 2021, p. 828-831. 4. A. Schlösser, et al.: “Simple Photonic Analysis of AES,” Journal of Cryptographic Engineering, 3 (1), 2013, p. 3-15. 5. F. Stellari, A.B. Shehata and P. Song: “1D and 2D Time-Resolved Emission Measurements of Circuits Fabricated in 14 nmTechnology Node,” Proc. Int. Symp. Phys. Failure Anal. Integr. Circuits (IPFA), 2020, p. 1-9. 6. P. Perdu, et al.: “Optical Probing (EOFM/TRI): A Large Set of ComplementaryApplications forUltimateVLSI,” Proc. Int. Symp. Phys. Failure Anal. Integr. Circuits (IPFA) , 2013, p. 119-126. 7. F. Stellari, et al.: “Time-resolved Imaging of VLSI Circuits using a Single-Photon Detector and a Scanning Head,” Proc. Int. Symp. Test. Fail. Anal. (ISTFA), 2019, p. 60-67. 8. F. Lan, F. Stellari, A.B. Shehata, andP.Song: “Extending theResolution of Emission Images BeyondDiffraction Limits using Deconvolution,” Proc. Int. Symp. Test. Fail. Anal. (ISTFA), 2016, p. 1-7. 9. I. Vogt, et al.: “New Method for Enhancing Photon Emission Measurements Similar to 2D-tomography,” Proc. Int. Symp. Phys. Failure Anal. Integr. Circuits (IPFA), 2018, p. 1-6. 10. F. Stellari, et al.: “Tester-based Methods to Enhance Spatial Resolvability and Interpretation of Time-integrated and Time- resolved Emission Measurements,” Proc. Int. Symp. Test. Fail. Anal. (ISTFA), 2013, p. 341-349. 11. S. Chef, et al.: “New Statistical Post Processing Approach for Precise Fault and Defect Localization in TRI Database Acquired on Complex VLSI,” Proc. Int. Symp. Phys. Failure Anal. Integr. Circuits (IPFA), 2013, p. 136-141. 12. S. Chef, et al.: “Cluster Matching in Time-resolved Imaging for VLSI Analysis.” Proc. Int. Symp. Phys. Failure Anal. Integr. Circuits (IPFA), 2014, p. 379-382. 13. H.P. Kriegel, P. Kröger, J. Sander, and A. Zimek: “Density-Based Clustering,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1 (3), 2011, p. 231-240. 14. M. Ester, H.P. Kriegel, J. Sander, and X. Xu: “ADensity-basedAlgorithm for Discovering Clusters in Large Spatial DatabaseswithNoise,” Proc. Int. Conf. Knowl. Disc. Data Mining, 1996, p. 226-231. 15. D.Briant and A. Kut: “ST-DBSCAN: An Algorithm for Clustering Spatial–temporal Data,” Data & Knowledge Engineering, 60 (1), 2007, p. 208-221. 16. https://www.idquantique.com/single-photon-systems/products/ id230/, IDQuantique ID 230 Brochure. 17. DS180 : 7 Series FPGA Data sheet: Overview, Xilinx Inc, https://www. xilinx.com/support /documentation/ data_sheets/ ds180_7Series_ Overview.pdf. 18. Skoll Kintex 7 User’s manual, Numato Lab, https://numato.com/ docs/skoll-kintex-7-fpga-module/. ABOUT THE AUTHORS Samuel Chef is a senior research scientist in Nanyang Technological University, Singapore. He holds a Ph.D. in image processing and instrumentation from the University of Burgundy, France. His research interests focus on the use and enhancement of signals acquired from optical techniques for IC analysis. Chung Tah Chua holds a Ph.D. from the school of materials science and engineering of Nanyang Technological University, Singapore, and is currently a research scientist in Temasek Laboratories @ NTU, Singapore. His research interest includes microelectronics hardware assurance and effects of space radiation on reliability. Chee Lip Gan is a professor at the school of materials science and engineer- ing, Nanyang Technological University. He is currently the associate provost and executive director of the office of research and technology in defense and security. Gan received his B.Eng. (electrical) from the National University of Singapore in 1999, and a Ph.D. in advancedmateri- als for micro- and nano-systems under the Singapore-MIT Alliance Program in 2003.