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Design and electrical simulation of on-chip neural learning based on nanocomponents

Design and electrical simulation of on-chip neural learning based on nanocomponents

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A neural inspired lookup table for reconfigurable circuits is described and simulated. The design is based on conductive bridge RAM to implement the synapses and carbon nanotube field effect transistors (CNTFET) for the other parts. Electrical simulations demonstrate compatibility between the nanocomponents and show the successful training of a linearly separable logical function NOR3.

References

    1. 1)
      • Widrow, B., Hoff, M.E.: `Adaptive switching circuits', 1960 WESCON Convention Record, Part IV, p. 96–104.
    2. 2)
    3. 3)
      • Kund, M., Beitel, G., Pinnow, C.-U., Rohr, T., Schumann, J., Symanczyk, R., Ufert, K., Muller, G.: `Conductive bridging RAM (CBRAM): an emerging non-volatile memory technology scalable to sub 20 nm', IEEE Int. Electron Devices Meeting, 2005, IEDM Tech. Dig., December 2005, p. 754–757.
    4. 4)
    5. 5)
      • Maneux, C., Goguet, J., Fregonese, S., Zimmer, T., D'Honincthun, H.C., Galdin-Retailleau, S.: `Analysis of CNTFET physical compact model', Int. Conf. on Design and Test of Integrated Systems in Nanoscale Technology (DTIS), 2006, p. 40–45.
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