Advertisement

Modeling Information Processing Using Nonidentical Coulomb Blockade Nanostructures

  • Javier CerveraEmail author
  • José M. Claver
  • Salvador Mafé
Conference paper
Part of the Advances in Atom and Single Molecule Machines book series (AASMM)

Abstract

In recent years, molecular-protected metallic nanoparticles (NPs) have attracted a great deal of attention. Because of their reduced size, they behave like tiny capacitors so that there is an energy penalty when adding an electron to the NP which suppresses the electric current at a potential lower than a threshold value. This phenomenon is known as Coulomb blockade (CB) and allows the transport of electrons to be modulated through an external gate provided that the energy penalty is higher than the thermal energy. Together with the possibility of tailoring their properties, molecular protected NPs are potential candidates as future components of high density, low consumption electronics. However, they face a number of problems before they can be considered as a technological viable option. To be used at room temperatures, NPs radii need to be in the nanometer range, and then fabrication processes lead to significant variability in the NPs physical properties. We use here two systems, a XOR gate and a R-SET model which mimics some characteristics of neurons, to show strategies that may be used to cope with the variability problem so that a robust information processing can be achieved despite using nominally different components.

Keywords

Nanoparticles Coulomb blockade Single electron transistor Variability Information processing 

References

  1. 1.
    Allan, A., Edenfeld, D., Joyner, W.H., Kahng, A.B., Rodgers, M., Zorian, Y.: 2001 technology roadmap for semiconductors. Computer 35, 42–53 (2002). doi: 10.1109/2.976918 CrossRefGoogle Scholar
  2. 2.
    Awschalom, D.D., Flatte, M.E.: Challenges for semiconductor spintronics. Nat. Phys. 3, 153–159 (2007). doi: 10.1038/nphys551 CrossRefGoogle Scholar
  3. 3.
    Bichler, O., Zhao, W., Alibart, F., Pleutin, S., Vuillaume, D., Gamrat, C.: Functional model of a nanoparticle organic memory transistor for use as a spiking synapse. IEEE Trans. Electron Devices 57(11), 3115–3122 (2010). doi:10.1109/TED.2010.2065951
  4. 4.
    Bohr, M.T.: Nanotechnology goals and challenges for electronic applications. IEEE Trans. Nanotechnol. 1(1), 56–62 (2002). doi: 10.1109/TNANO.2002.1005426 CrossRefGoogle Scholar
  5. 5.
    Brousseau, L.C., Zhao, Q., Shultz, D.A., Feldheim, D.L.: ph-gated single-electron tunneling in chemically modified gold nanoclusters. J. Am. Chem. Soc. 120(30), 7645–7646 (1998). doi: 10.1021/ja981262s CrossRefGoogle Scholar
  6. 6.
    Brust, M., Walker, M., Bethell, B., Schiffrin, D.J., Whyman, R.: Synthesis of thiol-derivatized gold nanoparticles in a 2-phase liquid-liquid system. J. Chem. Soc. Chem. Commun. 7, 801–802 (1994). doi: 10.1039/c39940000801 CrossRefGoogle Scholar
  7. 7.
    Cervera, J., Claver, J.M., Mafe, S.: Individual variability and average reliability in parallel networks of heterogeneous biological and artificial nanostructures. IEEE Trans. Nanotechnol. 12(6), 1198–1205 (2013). doi: 10.1109/TNANO.2013.2283871 CrossRefGoogle Scholar
  8. 8.
    Cervera, J., Mafe, S.: Multivalued and reversible logic gates implemented with metallic nanoparticles and organic ligands. Chemphyschem 11(8), 1654–1658 (2010). doi: 10.1002/cphc.200900973 CrossRefGoogle Scholar
  9. 9.
    Cervera, J., Mafe, S.: Information processing schemes based on monolayer protected metallic nanoclusters. J. Nanosc. Nanotechnol. 11(9), 7537–7548 (2011). doi: 10.1166/jnn.2011.4743 CrossRefGoogle Scholar
  10. 10.
    Cervera, J., Manzanares, J.A., Mafe, S.: Sub-threshold signal processing in arrays of non-identical nanostructures. Nanotechnology 22(43), 435,201 (2011). doi: 10.1088/0957-4484/22/43/435201
  11. 11.
    Cervera, J., Manzanares, J.A., Mafe, S.: Bio-inspired signal transduction with heterogeneous networks of nanoscillators. Appl. Phys. Lett. 100(9), 093,703 (2012). doi: 10.1063/1.3691630
  12. 12.
    Cervera, J., Manzanares, J.A., Mafe, S.: Biologically inspired information processing and synchronization in ensembles of non-identical threshold-potential nanostructures. Plos One 8(1), e53,821 (2013). doi: 10.1371/journal.pone.0053821
  13. 13.
    Chaki, N.K., Kakade, B., Vijayamohanan, K.P., Singh, P., Dharmadhikari, C.V.: Investigation of interparticle interactions of larger (4.63 nm) monolayer protected gold clusters during quantized double layer charging. Phys. Chem. Chem. Phys. 8(15), 1837–1844 (2006). doi: 10.1039/b516650k CrossRefGoogle Scholar
  14. 14.
    Chen, S.W., Ingram, R.S., Hostetler, M.J., Pietron, J.J., Murray, R.W., Schaaff, T.G., Khoury, J.T., Alvarez, M.M., Whetten, R.L.: Gold nanoelectrodes of varied size: transition to molecule-like charging. Science 280(5372), 2098–2101 (1998). doi: 10.1126/science.280.5372.2098 CrossRefGoogle Scholar
  15. 15.
    Daniel, M.C., Astruc, D.: Gold nanoparticles: assembly, supramolecular chemistry, quantum-size-related properties, and applications toward biology, catalysis, and nanotechnology. Chem. Rev. 104, 293–346 (2004). doi: 10.1021/cr030698+ CrossRefGoogle Scholar
  16. 16.
    Garcia-Morales, V., Mafe, S.: Monolayer-protected metallic nanoparticles: limitations of the concentric sphere capacitor model. J. Phys. Chem. C 111(20), 7242–7250 (2007). doi: 10.1021/jp067920+ CrossRefGoogle Scholar
  17. 17.
    de Gyvez, J.P., Tuinhout, H.P.: Threshold voltage mismatch and intra-die leakage current in digital CMOS circuits. IEEE J. Solid-state Circuits 39(1), 157–168 (2004). doi: 10.1109/JSSC.2003.820873 CrossRefGoogle Scholar
  18. 18.
    Hirano, Y., Segawa, Y., Yamada, F., Kuroda-Sowa, T., Kawai, T., Matsumoto, T.: Mn-12 molecular redox array exhibiting one-dimensional coulomb blockade behavior. J. Phys. Chem. C 116(18), 9895–9899 (2012). doi: 10.1021/jp301778r CrossRefGoogle Scholar
  19. 19.
    Jehl, X., Roche, B., Sanquer, M., Voisin, B., Wacquez, R., Deshpande, V., Previtali, B., Vinet, M., Verduijn, J., Tettamanzi, G., Rogge, S., Kotekar-Patil, D., Ruoff, M., Kern, D., Wharam, D., Belli, M., Prati, E., Fanciulli, M.: Mass production of silicon mos-sets: can we live with nano-devices variability? Proc. Comput. Sci. 7, 266–268 (2011). doi: 10.1016/j.procs.2011.09.016. Proceedings of the 2nd European Future Technologies Conference and Exhibition 2011 (FET 11)
  20. 20.
    Joachim, C., Gimzewski, J.K., Aviram, A.: Electronics using hybrid-molecular and mono-molecular devices. Nature 408(6812), 541–548 (2000). doi: 10.1038/35046000 CrossRefGoogle Scholar
  21. 21.
    Kane, J., Ong, J., Saraf, R.F.: Chemistry, physics, and engineering of electrically percolating arrays of nanoparticles: a mini review. J. Mater. Chem. 21, 16846–16858 (2011). doi: 10.1039/c1jm12005k CrossRefGoogle Scholar
  22. 22.
    Kano, S., Azuma, Y., Kanehara, M., Teranishi, T., Majima, Y.: Room-temperature Coulomb blockade from chemically synthesized au nanoparticles stabilized by acid-base interaction. Appl. Phys. Express 3(10), 105,003 (2010). doi: 10.1143/APEX.3.105003
  23. 23.
    Kano, S., Tada, T., Majima, Y.: Nanoparticle characterization based on STM and sts. Chem. Soc. Rev. 44(4), 970–987 (2015). doi: 10.1039/c4cs00204k CrossRefGoogle Scholar
  24. 24.
    Kikombo, A.K., Asai, T.: Bio-inspired single-electron circuit architectures exploiting thermal noises and device fluctuations to enhance signal transmission fidelity. In: 2009 International Symposium On Intelligent Signal Processing Communication Systems (ispacs 2009), pp. 429–432 (2009). doi: 10.1109/ISPACS.2009.5383809
  25. 25.
    Kikombo, A.K., Oya, T., Asai, T., Amemiya, Y.: Discrete dynamical systems consisting of single-electron circuits. Int. J. Bifurc. Chaos 17(10), 3613–3617 (2007). doi: 10.1142/S0218127407019457 CrossRefGoogle Scholar
  26. 26.
    Kish, L.B.: End of moore’s law: thermal (noise) death of integration in micro and nano electronics. Phys. Lett. A 305(3–4), 144–149 (2002). doi: 10.1016/S0375-9601(02)01365-8 CrossRefGoogle Scholar
  27. 27.
    Li, Z.Y.: Optics and photonics at nanoscale: principles and perspectives. EPL (Europhys. Lett.) 110(1), 14,001 (2015). doi: 10.1209/0295-5075/110/14001
  28. 28.
    Likharev, K.K.: Single-electron devices and their applications. Proc. IEEE 87(4), 606–632 (1999). doi: 10.1109/5.752518 CrossRefGoogle Scholar
  29. 29.
    Lin, B.J.: Making lithography work for the 7-nm node and beyond in overlay accuracy, resolution, defect, and cost. Microelectron. Eng. 143, 91–101 (2015). doi: 10.1016/j.mee.2015.04.033 CrossRefGoogle Scholar
  30. 30.
    Luo, K., Chae, D.H., Yao, Z.: Room-temperature single-electron transistors using alkanedithiols. Nanotechnology 18(46), 465,203 (2007). doi: 10.1088/0957-4484/18/46/465203
  31. 31.
    Maeda, K., Okabayashi, N., Kano, S., Takeshita, S., Tanaka, D., Sakamoto, M., Teranishi, T., Majima, Y.: Logic operations of chemically assembled single-electron transistor. Acs Nano 6(3), 2798–2803 (2012). doi: 10.1021/nn3003086 CrossRefGoogle Scholar
  32. 32.
    Martorell, F., Cotofana, S.D., Rubio, A.: An analysis of internal parameter variations effects on nanoscaled gates. IEEE Trans. Nanotechnol. 7, 24–33 (2008). doi: 10.1109/TNANO.2007.913429 CrossRefGoogle Scholar
  33. 33.
    McGuinness, J., Graham, B.P.: The role and function of noise and neural heterogeneity in the integrated population response of the vestibulo-ocular reflex. BMC Neurosci. 12, 127 (2011). doi: 10.1186/1471-2202-12-S1-P127 CrossRefGoogle Scholar
  34. 34.
    Miura, A., Tsukamoto, R., Yoshii, S., Yamashita, I., Uraoka, Y., Fuyuki, T.: Non-volatile flash memory with discrete bionanodot floating gate assembled by protein template. Nanotechnology 19(25), 255,201 (2008). doi: 10.1088/0957-4484/19/25/255201
  35. 35.
    Okabayashi, N., Maeda, K., Muraki, T., Tanaka, D., Sakamoto, M., Teranishi, T., Majima, Y.: Uniform charging energy of single-electron transistors by using size-controlled Au nanoparticles. Appl. Phys. Lett. 100(3), 033,101 (2012). doi: 10.1063/1.3676191
  36. 36.
    Oya, T., Asai, T., Amemiya, Y.: A single-electron reaction-diffusion device for computation of a Voronoi diagram. Int. J. Unconv. Comput. 3(4), 271–284 (2007)Google Scholar
  37. 37.
    Oya, T., Asai, T., Amemiya, Y.: Stochastic resonance in an ensemble of single-electron neuromorphic devices and its application to competitive neural networks. Chaos Solitons Fractals 32(2), 855–861 (2007). doi: 10.1016/j.chaos.2005.11.027 CrossRefGoogle Scholar
  38. 38.
    Padmanabhan, K., Urban, N.N.: Intrinsic biophysical diversity decorrelates neuronal firing while increasing information content. Nat. Neurosci. 13(10), 1276–1282 (2010). doi: 10.1038/nn.2630 CrossRefGoogle Scholar
  39. 39.
    Pillow, J.W., Ahmadian, Y., Paninski, L.: Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains. Neural Comput. 23(1), 1–45 (2011). doi: 10.1162/NECO_a_00058 CrossRefGoogle Scholar
  40. 40.
    Quinn, B.M., Liljeroth, P., Ruiz, V., Laaksonen, T., Kontturi, K.: Electrochemical resolution of 15 oxidation states for monolayer protected gold nanoparticles. J. Am. Chem. Soc. 125(22), 6644–6645 (2003). doi: 10.1021/ja0349305 CrossRefGoogle Scholar
  41. 41.
    Shadlen, M.N.: Rate versus temporal coding models. In: Nadel, L. (ed.) Encyclopedia of Cognitive Science, pp. 819–825. Macmillan, London (2002)Google Scholar
  42. 42.
    Stein, R.B., Gossen, E.R., Jones, K.E.: Neuronal variability: noise or part of the signal? Nat. Rev. Neurosc. 6(5), 389–397 (2005). doi: 10.1038/nrn1668 CrossRefGoogle Scholar
  43. 43.
    Stolarczyk, K., Bilewicz, R.: Electron transport through alkanethiolate films decorated with monolayer protected gold clusters. Electrochim. Acta 51(11), 2358–2365 (2006). doi: 10.1016/j.electacta.2005.03.091 CrossRefGoogle Scholar
  44. 44.
    Thorpe, S., Delorme, A., Rullen, R.V.: Spike-based strategies for rapid processing. Neural Netw. 14(6–7), 715–725 (2001). doi: 10.1016/S0893-6080(01)00083-1 CrossRefGoogle Scholar
  45. 45.
    Voter, A.F.: Introduction to the kinetic monte carlo method. In: Sickfus, K.E., Kotomin, E.A., Uberuaga, B.P. (eds.) Radiation Effects in Solids, vol. 235, pp. 1–23. Springer, Dordrecht (2007)CrossRefGoogle Scholar
  46. 46.
    Wang, K.L., Galatsis, K., Ostroumov, R., Khitun, A., Zhao, Z., Han, S.: Nanoarchitectonics for heterogeneous integrated nanosystems. Proc. IEEE 96(2), 212–229 (2008). doi: 10.1109/JPROC.2007.911055 CrossRefGoogle Scholar
  47. 47.
    White, J.A., Rubinstein, J.T., Kay, A.R.: Channel noise in neurons. Trends Neurosci. 23(3), 131–137 (2000). doi: 10.1016/S0166-2236(99)01521-0 CrossRefGoogle Scholar
  48. 48.
    Wong, H.S.P., Frank, D.J., Solomon, P.M., Wann, C.H.J., Welser, J.J.: Nanoscale cmos. Proc. IEEE 87(4), 537–570 (1999). doi: 10.1109/5.752515 CrossRefGoogle Scholar
  49. 49.
    Yarom, Y., Hounsgaard, J.: Voltage fluctuations in neurons: signal or noise? Physiol. Rev. 91(3), 917–929 (2011). doi: 10.1152/physrev.00019.2010 CrossRefGoogle Scholar
  50. 50.
    Zhang, M., Knoch, J., Zhang, S.L., Feste, S., Schroeter, M., Mantl, S.: Threshold voltage variation in SOI Schottky-barrier mosfets. IEEE Trans. Electron Devices 55(3), 858–865 (2008). doi: 10.1109/7ED.2007.915054 CrossRefGoogle Scholar
  51. 51.
    Zutic, I., Fabian, J., Sarma, S.D.: Spintronics: fundamentals and applications. Rev. Mod. Phys. 76, 323–410 (2004). doi: 10.1103/RevModPhys. 76.323 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Javier Cervera
    • 1
    Email author
  • José M. Claver
    • 2
  • Salvador Mafé
    • 1
  1. 1.Faculty of PhysicsUniversity of ValenciaValenciaSpain
  2. 2.School of EngineeringUniversity of ValenciaValenciaSpain

Personalised recommendations