Abstract
This paper presents the application of quantum dot gate nonvolatile memory (QDNVM) in image processing application. The charge accumulation in the gate region varies the threshold voltage of QDNVM, which can be used as a reference voltage source in a comparator circuit. A simplified comparator circuit can be implemented using the QDNVM. In this work, the use of QDNVM-based comparators in image processing specially image segmentation is demonstrated, which can be efficient in future image processing application.
















Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Sen, S., Capasso, F., Cho, A.Y., Sivco, D.L.: Multiple-state resonant-tunneling bipolar transistor operating at room temperature and its application as a frequency multiplier. IEEE Electron Device Lett. 9, 533 (1998)
Reed, M.A., Frensley, W.R., Matyi, R.J., Randall, J.N., Seabauggh, A.C.: Realization of a three terminal resonant tunneling device: the bipolar quantum resonant tunneling transistor. Appl. Phys. Lett. 54, 1034 (1989)
Uemura, T., Mazumder, P.: Design and analysis of resonant-tunneling-diode (RTD) based high performance memory system. IEICE Trans. Electron. E82–C, 1630 (1999)
Lin, H.C.: Resonant tunneling diodes for multi-valued digital applications. In: Proceedings of 24th IEEE international symposium multiple-valued logic, pp. 188–195 (1994)
Mazumder, P., Kulkarni, S., Bhattacharya, M., Sun, J.P., Haddad, G.I.: Digital circuit applications of resonant tunneling devices. Proc. IEEE 86, 664 (1998)
Lee, K.W., Sze, P.W., Lin, Y.J., Ying, N., Houng, M.P., Wang, Y.H.: InGaP/InGaAs metal-oxide-semiconductor pseudomorphic high-electron-mobility transistor with a liquid-phase-oxidized InGaP as gate dielectric. IEEE Electron Device Lett. 26, 864 (2005)
Karmakar, S., Suarez, E., Gogna, M., Jain, F.: ZnS-ZnMgS-ZnS lattice matched gate insulator as an alternative for silicon dioxide on silicon in quantum dot gate FETs (QDGFETs). J. Electron. Mater. 41, 2663–2670 (2012)
Velampati, R.: Quantum dot nonvolatile memory: modeling and fabrication. PhD thesis, University of Connecticut (2007)
Gogna, M., Suarez, E., Chan, P.Y., Al-Amoody, F., Karmakar, S., Jain, F.: Nonvolatile silicon memory using GeO x-cladded Ge quantum dots self-assembled on \(\text{ SiO }_{2}\) and lattice-matched II–VI tunnel insulator. J. Electron. Mater. 40, 1769–1774 (2011)
Phely-Bobin, T., Chattopadhyay, D., Papadimitrakopoulos, F.: Characterization of mechanically attrited Si/SiOx nanoparticles and their self-assembled composite films. Chem. Mater. 14, 1030–1036 (2002)
Jain, F., Papadimitrakopoulos, F.: Site-specific nanoparticle self-assembly. US Patent 7,368,370, (2008)
Karmakar, S., Chandy, J.A., Gogna, M., Jain, F.C.: Fabrication and circuit modeling of NMOS inverter based on quantum dot gate field-effect transistors. J. Electron. Mater. 41, 2184–2192 (2012)
Karmakar, S., Gogna, M., Jain, F.C.: Improved device structure of quantum dot gate FET to obtain more stable intermediate state. Electron. Lett. 48, 1556–1557 (2012)
Karmakar, S., Chandy, J.A., Jain, F.C.: Design of ternary logic combinational circuits based on quantum dot gate FETs. IEEE Trans. Very Large Scale Integr. Syst. 21, 793–806 (2012)
Karmakar, S., Suresh, A.P, Chandy, J.A., Jain, F.C.: Design of ADCs and DACs using 3-state quantum dot gate FETs. In: International semiconductor device research symposium, College Park, MD, 9–11 Dec (2009)
Arora, S., Acharya, J., Verma, A., Panigrahi, P.K.: Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. Pattern Recognit. Lett. 29, 119–125 (2008)
Al-amri, S.S., Kalyankar, N.V., Khamitkar, S.D.: Image segmentation by using threshold techniques. J. Comput. 2, 83–86 (2010)
Papamarkos, N., Strouthopoulos, C., Andreadis, I.: Multithresholding of color and gray-level images through a neural network technique. Image Vis. Comput. 18, 213–222 (2000)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceeding of 8th international conference on computer vision, vol. 2, pp. 416–423 (2001)
Meila, M.: Comparing clusterings: an axiomatic view. In: Proceeding of international on conference on machine learning, Bonn, Germany, pp. 577–584 (2005)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59, 167–181 (2004)
Pantofaru, C., Hebert, M.: A comparison of image segmentation algorithms. Robotics Institute, Carnegie Mellon University, Tech. Rep. CMU-RI-TR-05-40 (2005)
An, N.-Y., Pun, C.-M.: Color image segmentation using adaptive color quantization and multiresolution texture characterization. Signal Image Video Process. 8, 943–954 (2014)
Zhao, Y., Liu, J., Li, H., Li., G.: Improved watershed algorithm for dowels image segmentation. In: Proceedings of the 7th world congress on intelligent control and automation (2008)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Patterns Anal. Mach. Intell. 22, 888–905 (2000)
Tao, W., Jin, H., Zhang, Y., Liu, L., Wang, D.: Image thresholding using graph cuts. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 38, 1181–1195 (2008)
Levin, A.: Blind motion deblurring using image statistics. Adv. Neural Inf. Process. Syst. 19, 841–848 (2006)
Liu, R., Li, Z., Jia, J.: Image partial blur detection and classification. In: Computer vision and pattern recognition, CVPR 2008. IEEE conference on 2008, pp. 1–8 (2008)
Su, B., Lu, S., Tan, C.L.: Blurred image region detection and classification. In: Presented at the proceedings of the 19th ACM international conference on multimedia, Scottsdale, Arizona, USA (2011)
Wang, W., Zheng, J.-J., Zhou, H.-J.: Segmentation, removing and ranking partial blur. Signal Image Video Process. 8, 647–655 (2014). doi:10.1007/s11760-013-0573-8
Acknowledgments
QDNVM was fabricated by Micro-Opto Electronics Lab members in the University of Connecticut, where author was in a leading role. The development of QDNVM circuit model as well as its application in image segmentation was done by author individually after finishing his doctoral degree.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Karmakar, S., Gogna, M. & Jain, F.C. Application of quantum dot gate nonvolatile memory (QDNVM) in image segmentation. SIViP 10, 551–558 (2016). https://doi.org/10.1007/s11760-015-0773-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-015-0773-5