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Afifi, A., Ayatollahi, A. and Raissi, F. (2009) Implementation of Biologically Plausible Spiking Neural Network Models on the Memristor Crossbar-Based CMOS/Nano Circuits. European Conference on Circuit Theory and Design, ECCTD 2009, Antalya, 23-27 August 2009, 563-566.

has been cited by the following article:

• TITLE: On Design of Memristive Amplifier Circuits

  AUTHORS: Timur Ibrayev, Irina Fedorova, Akshay Kumar Maan, Alex Pappachen James

  KEYWORDS: Resistive Switching, Memristive Device, Quantized Conductance, Common Source Amplifier, Common Drain Amplifier, Differential Amplifier

  JOURNAL NAME: Circuits and Systems, Vol.5 No.11, October 22, 2014

  ABSTRACT: Amplifiers are essential building blocks of a majority of the mixed signal circuits that are used in the development of cognitive computing architectures. Their implementation and use is challenged by the second order effects that dominate the MOSFET operations with reduction in the technology size and scale. The ability to program the amplifiers once fabricated becomes an even more challenging problem as it warrants the use of multiple circuit components that lowers circuit performance and in turn outweighs the advantages of generalisation abilities. In this paper, a reconfigurable set of amplifier circuits are proposed based on quantised conductance devices in combination with MOSFET devices. The presented circuits form the basic configurations for the memristor based amplifiers, and show promising performance results in terms of power dissipation, on-chip area and THD values.