Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-26T07:41:41.230Z Has data issue: false hasContentIssue false
  • English
  • Français

Image Segmentation for FIB-SEM Serial Sectioning of a Si/C–Graphite Composite Anode Microstructure Based on Preprocessing and Global Thresholding

Published online by Cambridge University Press:  07 August 2019

Dongjae Kim Open the ORCID record for Dongjae Kim [Opens in a new window] ,
Sihyung Lee ,
Wooram Hong ,
Hyosug Lee ,
Seongho Jeon ,
Sungsoo Han  and
Jaewook Nam Open the ORCID record for Jaewook Nam [Opens in a new window]
Dongjae Kim
Affiliation:
School of Chemical and Biological Engineering, Seoul National University, Seoul 08826, Republic of Korea
Sihyung Lee
Affiliation:
Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., Suwon 16677, Republic of Korea
Wooram Hong
Affiliation:
Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., Suwon 16677, Republic of Korea
Hyosug Lee
Affiliation:
Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., Suwon 16677, Republic of Korea
Seongho Jeon
Affiliation:
Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., Suwon 16677, Republic of Korea
Sungsoo Han
Affiliation:
Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., Suwon 16677, Republic of Korea
Jaewook Nam*
Affiliation:
School of Chemical and Biological Engineering, Seoul National University, Seoul 08826, Republic of Korea Institute of Chemical Process, Seoul National University, Seoul 08826, Republic of Korea
*
*Author for correspondence: Jaewook Nam, E-mail: jaewooknam@snu.ac.kr
Get access

Abstract

The choice of materials that constitute electrodes and the way they are interconnected, i.e., the microstructure, influences the performance of lithium-ion batteries. For batteries with high energy and power densities, the microstructure of the electrodes must be controlled during their manufacturing process. Moreover, understanding the microstructure helps in designing a high-performance, yet low-cost battery. In this study, we propose a systematic algorithm workflow for the images of the microstructure of anodes obtained from a focused ion beam scanning electron microscope (FIB-SEM). Here, we discuss the typical issues that arise in the raw FIB-SEM images and the corresponding preprocessing methods that resolve them. Next, we propose a Fourier transform-based filter that effectively reduces curtain artifacts. Also, we propose a simple, yet an effective, global-thresholding method to identify active materials and pores in the microstructure. Finally, we reconstruct the three-dimensional structures by concatenating the segmented images. The whole algorithm workflow used in this study is not fully automated and requires user interactions such as choosing the values of parameters and removing shine-through artifacts manually. However, it should be emphasized that the proposed global-thresholding method is deterministic and stable, which results in high segmentation performance for all sectioning images.

Keywords

anode microstructureFIB-SEMglobal thresholdingLi-ion batterypreprocessing
Type
Materials Applications
Information
Microscopy and Microanalysis , Volume 25 , Issue 5 , October 2019 , pp. 1139 - 1154
Copyright
Copyright © Microscopy Society of America 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

†

These authors equally contributed to this work.

References

Armand, M & Tarascon, JM (2008). Building better batteries. Nature 451, 652–657.10.1038/451652aGoogle Scholar
Bay, H, Tuytelaars, T, Van Gool, L & Pinz, A (2006). Surf: Speeded up robust features. In European Conference on Computer Vision, Leonardis, A & Bischof, H (Eds.), pp. 404–417. Heidelberg, Germany: Springer.Google Scholar
Brundle, CR, Evans, CA & Wilson, S (1992). Encyclopedia of Materials Characterization: Surfaces, Interfaces, Thin Films. Houston, TX: Gulf Professional Publishing.Google Scholar
Carpenter, GJC & Wronski, Z (2015). Microscopy techniques for analysis of nickel metal hydride batteries constituents. Microsc Microanal 21(6), 1433–1442.10.1017/S1431927615015470Google Scholar
Chambolle, A (2004). An algorithm for total variation minimization and applications. J Math Imaging Vision 20(1–2), 89–97.Google Scholar
Chen, Z, Wang, X, Giuliani, F & Atkinson, A (2015). Microstructural characteristics and elastic modulus of porous solids. Acta Mater 89, 268–277.10.1016/j.actamat.2015.02.014Google Scholar
Chung, DW, Ebner, M, Ely, DR, Wood, V & García, RE (2013). Validity of the Bruggeman relation for porous electrodes. Model Simul Mater Sci 21(7), 074009.10.1088/0965-0393/21/7/074009Google Scholar
Chung, DW, Shearing, PR, Brandon, NP, Harris, SJ & García, RE (2014). Particle size polydispersity in Li-ion batteries. J Electrochem Soc 161(3), A422–A430.Google Scholar
Comer, ML & Delp, EJ (2000). The EM/MPM algorithm for segmentation of textured images: Analysis and further experimental results. IEEE Trans Image Process 9(10), 1731–1744.10.1109/83.869185Google Scholar
Cooper, S, Eastwood, D, Gelb, J, Damblanc, G, Brett, D, Bradley, R, Withers, P, Lee, P, Marquis, A, Brandon, N & Shearing, PR (2014). Image based modelling of microstructural heterogeneity in LiFePO4 electrodes for Li-ion batteries. J Power Sources 247, 1033–1039.10.1016/j.jpowsour.2013.04.156Google Scholar
Darling, R & Newman, J (1997). Modeling a porous intercalation electrode with two characteristic particle sizes. J Electrochem Soc 144(12), 4201–4208.10.1149/1.1838166Google Scholar
Denisyuk, A, Hrnčíř, T, Oboňa, JV, Petrenec, M & Michalička, J (2017). Mitigating curtaining artifacts during Ga FIB TEM lamella preparation of a 14 nm FinFET device. Microsc Microanal 23, 484–490.10.1017/S1431927617000241Google Scholar
Doyle, M, Fuller, TF & Newman, J (1993). Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. J Electrochem Soc 140(6), 1526–1533.Google Scholar
Duduta, M, Ho, B, Wood, VC, Limthongkul, P, Brunini, VE, Carter, WC & Chiang, YM (2011). Semi-solid lithium rechargeable flow battery. Adv Energy Mater 1(4), 511–516.10.1002/aenm.201100152Google Scholar
Dunn, B, Kamath, H & Tarascon, JM (2011). Electrical energy storage for the grid: A battery of choices. Science 334, 928–935.10.1126/science.1212741Google Scholar
Ebner, M, Geldmacher, F, Marone, F, Stampanoni, M & Wood, V (2013). X-ray tomography of porous, transition metal oxide based lithium ion battery electrodes. Adv Energy Mater 3(7), 845–850.10.1002/aenm.201200932Google Scholar
Ender, M, Joos, J, Carraro, T & Ivers-Tiffée, E (2011). Three-dimensional reconstruction of a composite cathode for lithium-ion cells. Electrochem Commun 13(2), 166–168.10.1016/j.elecom.2010.12.004Google Scholar
Etiemble, A, Besnard, N, Bonnin, A, Adrien, J, Douillard, T, Tran-Van, P, Gautier, L, Badot, JC, Marie, E & Lestriez, B (2017). Multiscale morphological characterization of process induced heterogeneities in blended positive electrodes for lithium–ion batteries. J Mater Sci 52(7), 3576–3596.10.1007/s10853-016-0374-xGoogle Scholar
Fitschen, JH, Ma, J & Schuff, S (2017). Removal of curtaining effects by a variational model with directional forward differences. Comput Vis Image Underst 155, 24–32.10.1016/j.cviu.2016.12.008Google Scholar
García-García, R & García, RE (2016). Microstructural effects on the average properties in porous battery electrodes. J Power Sources 309, 11–19.10.1016/j.jpowsour.2015.11.058Google Scholar
Ghamisi, P, Couceiro, MS, Benediktsson, JA & Ferreira, NM (2012). An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst Appl 39(16), 12407–12417.10.1016/j.eswa.2012.04.078Google Scholar
Giannuzzi, LA (2004). Introduction to Focused Ion Beams: Instrumentation, Theory, Techniques and Practice. Heidelberg, Germany: Springer Science & Business Media.Google Scholar
Goldstein, JI, Newbury, DE, Michael, JR, Ritchie, NWM, Schott, JHJ & Joy, DC (2017). Scanning Electron Microscopy and X-Ray Microanalysis. New York: Springer.Google Scholar
Gonzalez, RC & Woods, RE (2008). Digital Image Processing. Upper Saddle River, NJ: Pearson Education.Google Scholar
Gunda, NSK, Choi, HW, Berson, A, Kenney, B, Karan, K, Pharoah, JG & Mitra, SK (2011). Focused ion beam-scanning electron microscopy on solid-oxide fuel-cell electrode: Image analysis and computing effective transport properties. J Power Sources 196(7), 3592–3603.10.1016/j.jpowsour.2010.12.042Google Scholar
Harris, SJ, Timmons, A, Baker, DR & Monroe, C (2010). Direct in situ measurements of Li transport in Li-ion battery negative electrodes. Chem Phys Lett 485(4–6), 265–274.10.1016/j.cplett.2009.12.033Google Scholar
Huggins, RA (1989). Materials science principles related to alloys of potential use in rechargeable lithium cells. J Power Sources 26(1–2), 109–120.10.1016/0378-7753(89)80020-5Google Scholar
Hutzenlaub, T, Thiele, S, Paust, N, Spotnitz, R, Zengerle, R & Walchshofer, C (2014). Three-dimensional electrochemical Li-ion battery modelling featuring a focused ion-beam/scanning electron microscopy based three-phase reconstruction of a LiCoO2 cathode. Electrochim Acta 115, 131–139.10.1016/j.electacta.2013.10.103Google Scholar
Kang, B & Ceder, G (2009). Battery materials for ultrafast charging and discharging. Nature 458, 190–193.Google Scholar
Kehrwald, D, Shearing, PR, Brandon, NP, Sinha, PK & Harris, SJ (2011). Local tortuosity inhomogeneities in a lithium battery composite electrode. J Electrochem Soc 158(12), A1393–A1399.Google Scholar
Kim, D, Choi, J & Nam, J (2015). Image analysis for measuring rod network properties. Meas Sci Technol 26(12), 125601.10.1088/0957-0233/26/12/125601Google Scholar
Kim, D, Choi, J & Nam, J (2016). Entropy-assisted image segmentation for nano-and micro-sized networks. J Microsc 262(3), 274–294.10.1111/jmi.12362Google Scholar
Kishimoto, M, Iwai, H, Saito, M & Yoshida, H (2009). Quantitative evaluation of transport properties of SOFC porous anode by random walk process. ECS Trans 25(2), 1887–1896.10.1149/1.3205731Google Scholar
Kraytsberg, A & Ein-Eli, Y (2016). Conveying advanced Li-ion battery materials into practice the impact of electrode slurry preparation skills. Adv Energy Mater 6(21), 1600655.10.1002/aenm.201600655Google Scholar
Kulkarni, R, Tuller, M, Fink, W & Wildenschild, D (2012). Three-dimensional multiphase segmentation of X-ray CT data of porous materials using a Bayesian Markov random field framework. Vadose Zone J 11(1). doi:10.2136/vzj2011.0082.Google Scholar
Liao, PS, Chen, TS & Chung, PC (2001). A fast algorithm for multilevel thresholding. J Inf Sci Eng 17, 713–727.Google Scholar
Liu, Z, Verhallen, TW, Singh, DP, Wang, H, Wagemaker, M & Barnett, S (2016). Relating the 3D electrode morphology to Li-ion battery performance: A case for LiFePO4. J Power Sources 324, 358–367.10.1016/j.jpowsour.2016.05.097Google Scholar
Loeber, TH, Laegel, B, Wolff, S, Schuff, S, Balle, F, Beck, T, Eifler, D, Fitschen, JH & Steidl, G (2017). Reducing curtaining effects in FIB/SEM applications by a goniometer stage and an image processing method. J Vac Sci Technol 35(6), 06GK01.10.1116/1.4991638Google Scholar
Lu, L, Han, X, Li, J, Hua, J & Ouyang, M (2013). A review on the key issues for lithium-ion battery management in electric vehicles. J Power Sources 226, 272–288.10.1016/j.jpowsour.2012.10.060Google Scholar
Mangipudi, KR, Radisch, V, Holzer, L & Volkert, CA (2016). A FIB-nanotomography method for accurate 3D reconstruction of open nanoporous structures. Ultramicroscopy 163, 38–47.10.1016/j.ultramic.2016.01.004Google Scholar
Münch, B, Trtik, P, Marone, F & Stampanoni, M (2009). Stripe and ring artifact removal with combined wavelet—Fourier filtering. Opt Express 17(10), 8567–8591.10.1364/OE.17.008567Google Scholar
Ogata, K, Jeon, S, Ko, DS, Jung, IS, Kim, JH, Ito, K, Kubo, Y, Takei, K, Saito, S, Cho, YH, Park, H, Jang, J, Kim, HG, Kim, YS, Choi, W, Koh, M, Uosaki, K, Doo, SG, Hwang, Y & Han, S (2018). Evolving affinity between Coulombic reversibility and hysteretic phase transformations in nano-structured silicon-based lithium-ion batteries. Nat Commun 9(1), 479.Google Scholar
Oh, W & Lindquist, WB (1999). Image thresholding by indicator kriging. IEEE Trans Pattern Anal Mach Intell 21(7), 590–602.Google Scholar
Oleshko, VP, Herzing, AA, Soles, CL, Griebel, JJ, Chung, WJ, Simmonds, AG & Pyun, J (2016). Analytical multimode scanning and transmission electron imaging and tomography of multiscale structural architectures of sulfur copolymer-based composite cathodes for next-generation high-energy density Li–S batteries. Microsc Microanal 22(6), 1198–1221.10.1017/S1431927616011880Google Scholar
Oliva, D, Cuevas, E, Pajares, G, Zaldivar, D & Perez-Cisneros, M (2013). Multilevel thresholding segmentation based on harmony search optimization. J Appl Math 2013, 1–24.10.1155/2013/575414Google Scholar
Otsu, N (1979). A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9, 62–66.10.1109/TSMC.1979.4310076Google Scholar
Pham, TD (2001). Image segmentation using probabilistic fuzzy c-means clustering. IEEE Int Conf Image Process 1, 722–725.Google Scholar
Pietsch, P, Westhoff, D, Feinauer, J, Eller, J, Marone, F, Stampanoni, M, Schmidt, V & Wood, V (2016). Quantifying microstructural dynamics and electrochemical activity of graphite and silicon-graphite lithium ion battery anodes. Nat Commun 7, 12909.Google Scholar
Pikul, JH, Zhang, HG, Cho, J, Braun, PV & King, WP (2013). High-power lithium ion microbatteries from interdigitated three-dimensional bicontinuous nanoporous electrodes. Nat Commun 4, 1732.Google Scholar
Pinson, MB & Bazant, MZ (2013). Theory of SEI formation in rechargeable batteries: Capacity fade, accelerated aging and lifetime prediction. J Electrochem Soc 160(2), A243–A250.Google Scholar
Prill, T, Schladitz, K, Jeulin, D, Faessel, M & Wieser, C (2013). Morphological segmentation of FIB-SEM data of highly porous media. J Microsc 250(2), 77–87.Google Scholar
Promentilla, MAB, Sugiyama, T, Hitomi, T & Takeda, N (2008). Characterizing the 3D pore structure of hardened cement paste with synchrotron microtomography. J Adv Concr Technol 6(2), 273–286.Google Scholar
Raja, NSM, Rajinikanth, V & Latha, K (2014). Otsu based optimal multilevel image thresholding using firefly algorithm. Model Simul Eng 2014, 1–17.Google Scholar
Rudin, LI, Osher, S & Fatemi, E (1992). Nonlinear total variation based noise removal algorithms. Phys D: Nonlinear Phenom 60, 259–268.Google Scholar
Salzer, M, Spettl, A, Stenzel, O, Smått, JH, Lindén, M, Manke, I & Schmidt, V (2012). A two-stage approach to the segmentation of FIB-SEM images of highly porous materials. Mater Charact 69, 115–126.Google Scholar
Sarkar, S, Das, S, Paul, S, Polley, S, Burman, R & Chaudhuri, SS (2013). Multi-level image segmentation based on fuzzy-Tsallis entropy and differential evolution. IEEE Int Conf Fuzzy Syst, Hyderabad, India, October 2013, pp. 1–8. doi:10.1109/FUZZ-IEEE.2013.6622406.Google Scholar
Sauvola, J & Pietikäinen, M (2000). Adaptive document image binarization. Pattern Recognit 33(2), 225–236.Google Scholar
Schlüter, S, Sheppard, A, Brown, K & Wildenschild, D (2014). Image processing of multiphase images obtained via X-ray microtomography: A review. Water Resour Res 50(4), 3615–3639.Google Scholar
Sezen, M (2016). Focused Ion Beams (FIB)—Novel methodologies and recent applications for multidisciplinary sciences. In Modern Electron Microscopy in Physical and Life Sciences, Janecek, M (Ed.). Rijeka, Croatia: InTech.Google Scholar
Shearing, P, Golbert, J, Chater, RJ & Brandon, NP (2009). 3D reconstruction of SOFC anodes using a focused ion beam lift-out technique. Chem Eng Sci 64(17), 3928–3933.Google Scholar
Shearing, PR, Brandon, NP, Gelb, J, Bradley, R, Withers, PJ, Marquis, A, Cooper, S & Harris, SJ (2012). Multi length scale microstructural investigations of a commercially available Li-ion battery electrode. J Electrochem Soc 159(7), A1023–A1027.Google Scholar
Singh, M, Kaiser, J & Hahn, H (2015). Thick electrodes for high energy lithium ion batteries. J Electrochem Soc 162(7), A1196–A1201.Google Scholar
Singh, TR, Roy, S, Singh, OI, Sinam, T & Singh, KM (2011). A new local adaptive thresholding technique in binarization. Int J Comput Sci Issues 8, 271–277.Google Scholar
Sosa, JM, Huber, DE, Welk, B & Fraser, HL (2014). Development and application of MIPAR™: A novel software package for two-and three-dimensional microstructural characterization. Integr Mater Manuf Innov 3(1), 10.Google Scholar
Stephenson, DE, Hartman, EM, Harb, JN & Wheeler, DR (2007). Modeling of particle-particle interactions in porous cathodes for lithium-ion batteries. J Electrochem Soc 154(12), A1146–A1155.Google Scholar
Taiwo, OO, Finegan, DP, Gelb, J, Holzner, C, Brett, DJL & Shearing, PR (2016). The use of contrast enhancement techniques in X-ray imaging of lithium–ion battery electrodes. Chem Eng Sci 154, 27–33.Google Scholar
Tang, Y, Zhang, Y, Li, W, Ma, B & Chen, X (2015). Rational material design for ultrafast rechargeable lithium-ion batteries. Chem Soc Rev 44(17), 5926–5940.Google Scholar
Tarascon, JM (2010). Key challenges in future Li-battery research. Phil Trans R Soc A 368(1923), 3227–3241.Google Scholar
Tasdizen, T, Jurrus, E & Whitaker, RT (2008). Non-uniform illumination correction in transmission electron microscopy. MICCAI Workshop on Microscopic Image Analysis with Applications in Biology, New York, September 2008.Google Scholar
Thorat, IV, Stephenson, DE, Zacharias, NA, Zaghib, K, Harb, JN & Wheeler, DR (2009). Quantifying tortuosity in porous Li-ion battery materials. J Power Sources 188(2), 592–600.Google Scholar
Torr, PHS & Zisserman, A (2000). MLESAC: A new robust estimator with application to estimating image geometry. Comput Vis Image Underst 78(1), 138–156.Google Scholar
Unocic, RR, Sun, XG, Sacci, RL, Adamczyk, LA, Alsem, DH, Dai, S, Nj, D & More, KL (2014). Direct visualization of solid electrolyte interphase formation in lithium-ion batteries with in situ electrochemical transmission electron microscopy. Microsc Microanal 20(4), 1029–1037.Google Scholar
Vetter, J, Novák, P, Wagner, M, Veit, C, Möller, KC, Besenhard, JO, Winter, M, Wohlfahrt-Mehrens, M, Vogler, C & Hammouche, A (2005). Ageing mechanisms in lithium-ion batteries. J Power Sources 147(1-2), 269–281.Google Scholar
Vijayaraghavan, B, Ely, DR, Chiang, YM, García-García, R & García, RE (2012). An analytical method to determine tortuosity in rechargeable battery electrodes. J Electrochem Soc 159(5), A548–A552.Google Scholar
Volkert, CA & Minor, AM (2007). Focused ion beam microscopy and micromachining. MRS Bull 32(5), 389–399.Google Scholar
Wang, Z, Bovik, AC, Sheikh, HR & Simoncelli, EP (2004). Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 13(4), 600–612.Google Scholar
Wei, W (2013). Image binarization under non-uniform illumination based on gray-intensity wave equalization. In Image and Signal Processing (CISP), 2013 6th International Congress, Hangzhou, China, 16–18 December, IEEE, pp. 604–609.Google Scholar
Wiedemann, AH, Goldin, GM, Barnett, SA, Zhu, H & Kee, RJ (2013). Effects of three-dimensional cathode microstructure on the performance of lithium-ion battery cathodes. Electrochim Acta 88, 580–588.Google Scholar
Wieser, C, Prill, T & Schladitz, K (2015). Multiscale simulation process and application to additives in porous composite battery electrodes. J Power Sources 277, 64–75.Google Scholar
Yi, YB, Wang, CW & Sastry, AM (2006). Compression of packed particulate systems: Simulations and experiments in graphitic Li-ion anodes. J Eng Mater Technol 128(1), 73–80.Google Scholar
Supplementary material: File

Kim et al. supplementary material

Kim et al. supplementary material 1

Download Kim et al. supplementary material(File)
File 89.2 MB