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A STANDARD CELL LIBRARY USING CMOS TRANSCONDUCTANCE AMPLIFIERS FOR CELLULAR NEURAL NETWORKS
MAILAVARAM, MADHURI

2006, MS, University of Cincinnati, Engineering : Electrical Engineering.
Cellular Neural Networks (CNNs) form a class of information-processing systems which like neural networks are large-scale nonlinear analog circuits performing real time parallel processing of signals. Their local connectivity and regular architecture, unlike neural networks, make very efficient VLSI layouts, yielding higher chip densities, at very high operating speeds. Due to their continuous time feature, CNN’s form an alternative to conventional computers with great potential for image processing and pattern recognition applications. In the analog CMOS VLSI realization of CNN’s, inverter based CMOS transconductance amplifier forms a basic building block. By choosing the appropriate transconductance parameters, according to the predetermined coefficients, this approach can be adapted for various CNN applications, thus enabling a standard cell library realization for various image processing applications. Standard cell library is laid out in MAGIC layout editor with 0.5µ technology and simulations are carried out in HSPICE. The library includes LOGICNOT, LOGICOR, SHIFT, DILATION and FILBLACK applications. Additional cells can be designed in a similar manner. In addition, simulations were carried out in MATLAB for the above mentioned CNN applications.
Dr. Carla Purdy (Advisor)
205 p.

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MAILAVARAM, M. (2006). A STANDARD CELL LIBRARY USING CMOS TRANSCONDUCTANCE AMPLIFIERS FOR CELLULAR NEURAL NETWORKS. (Electronic Thesis or Dissertation). Retrieved from https://etd.ohiolink.edu/

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MAILAVARAM, MADHURI. "A STANDARD CELL LIBRARY USING CMOS TRANSCONDUCTANCE AMPLIFIERS FOR CELLULAR NEURAL NETWORKS." Electronic Thesis or Dissertation. University of Cincinnati, 2006. OhioLINK Electronic Theses and Dissertations Center. 22 Sep 2018.

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MAILAVARAM, MADHURI "A STANDARD CELL LIBRARY USING CMOS TRANSCONDUCTANCE AMPLIFIERS FOR CELLULAR NEURAL NETWORKS." Electronic Thesis or Dissertation. University of Cincinnati, 2006. https://etd.ohiolink.edu/

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