Tunneling-Based Cellular Nonlinear Network Architectures for Image Processing

The resonant tunneling diode (RTD) has found numerous applications in high-speed digital and analog circuits due to the key advantages associated with its folded back negative differential resistance (NDR) current-voltage (I-V) characteristics as well as its extremely small switching capacitance. Recently, the RTD has also been employed to implement high-speed and compact cellular neural/nonlinear networks (CNNs) by exploiting its quantum tunneling induced nonlinearity and symmetrical I-V characteristics for both positive and negative voltages applied across the anode and cathode terminals of the RTD. This paper proposes an RTD-based CNN architecture and investigates its operation through driving-point-plot analysis, stability and settling time study, and circuit simulation. Full-array simulation of a 128 times 128 RTD-based CNN for several image processing functions is performed using the Quantum Spice simulator designed at the University of Michigan, where the RTD is represented in SPICE simulator by a physics based model derived by solving Schrodinger's and Poisson's equations self-consistently. A comparative study between different CNN implementations reveals that the RTD-based CNN can be designed superior to conventional CMOS technologies in terms of integration density, operating speed, and functionality.

[1]  Jacek Kowalski.  0.8 μm CMOS implementation of weighted-order statistic image filter based on cellular neural network architecture , 2003, IEEE Trans. Neural Networks.

[2]  L.O. Chua,et al.  Cellular neural networks , 1993, 1988., IEEE International Symposium on Circuits and Systems.

[3]  Wolfgang Porod,et al.  Nanoelectronic single-electron transistor circuits and architectures: Research Articles , 2004 .

[4]  Leon O. Chua,et al.  Compact bistable CNNs based on resonant tunneling diodes , 2001, ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196).

[5]  T. Mizutani,et al.  Functions and applications of monostable-bistable transition logic elements (MOBILE's) having multiple-input terminals , 1994 .

[6]  R. Dogaru,et al.  A simple RTD-based circuit for Boolean CNN cells , 2000, Proceedings of the 2000 6th IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA 2000) (Cat. No.00TH8509).

[7]  Leon O. Chua,et al.  A compact and universal cellular neural network cell based on resonant tunneling diodes: circuit, model, and functional capabilities , 2000, Proceedings of the 2000 6th IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA 2000) (Cat. No.00TH8509).

[8]  Ángel Rodríguez-Vázquez,et al.  Toward visual microprocessors , 2002, Proc. IEEE.

[9]  Radu Dogaru,et al.  Simplicial RTD-Based Cellular Nonlinear Networks , 2003 .

[10]  Leon O. Chua,et al.  Cellular neural networks: applications , 1988 .

[11]  Leon O. Chua,et al.  On the implementation of RTD based CNNs , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

[12]  Sina Balkir,et al.  Nanostructure array of coupled RTDs as cellular neural networks , 2003, Int. J. Circuit Theory Appl..

[13]  Leon O. Chua,et al.  Rtd-Based Cellular Neural Networks with Multiple steady States , 2001, Int. J. Bifurc. Chaos.

[14]  Leon O. Chua,et al.  The CNN paradigm , 1993 .

[15]  Leon O. Chua,et al.  Methods for image processing and pattern formation in Cellular Neural Networks: a tutorial , 1995 .

[16]  Sina Balkir,et al.  Bio-inspired compact cell circuit for reaction-diffusion systems , 2005, IEEE Transactions on Circuits and Systems II: Express Briefs.

[17]  J. Laskar,et al.  RTD/CMOS nanoelectronic circuits: thin-film InP-based resonant tunneling diodes integrated with CMOS circuits , 1999, IEEE Electron Device Letters.

[18]  Takao Waho,et al.  Design of flash analog-to-digital converters using resonant-tunneling circuits : New system paradigms for integrated electronics , 2004 .

[19]  H.J. De Los Santos,et al.  Physics-based RTD current-voltage equation , 1996, IEEE Electron Device Letters.

[20]  Peter R. Kinget,et al.  A programmable analog cellular neural network CMOS chip for high speed image processing , 1995, IEEE J. Solid State Circuits.

[21]  L. Chua,et al.  Cellular neural/nonlinear networks using resonant tunneling diode , 2004, 4th IEEE Conference on Nanotechnology, 2004..

[22]  R. Dogaru,et al.  Physical modeling of RTD-based CNN cells , 2000, Proceedings of the 2000 6th IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA 2000) (Cat. No.00TH8509).

[23]  Pinaki Mazumder,et al.  Digital circuit applications of resonant tunneling devices , 1998, Proc. IEEE.

[24]  Sina Balkir,et al.  Computational paradigm for nanoelectronics: self-assembled quantum dot cellular neural networks , 2005 .

[25]  Wolfgang Porod,et al.  Nanoelectronic single-electron transistor circuits and architectures , 2004, Int. J. Circuit Theory Appl..

[26]  Leon O. Chua,et al.  Cellular neural networks based on resonant tunnelling diodes , 2001, Int. J. Circuit Theory Appl..

[27]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .

[28]  Yang Fuhua,et al.  Resonant tunnelling diodes and high electron mobility transistors integrated on GaAs substrates , 2006 .