Title :
A compact and universal cellular neural network cell based on resonant tunneling diodes: circuit, model, and functional capabilities
Author :
Dogaru, Radu ; Hänggi, Martin ; Chu, Leon O.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA
Abstract :
A novel cellular neural network (CNN) cell and its circuit realization are proposed. The theory of the multi-nested universal cell is applied, and the nonmonotonic current-voltage characteristic of resonant tunneling diodes (RTD) is exploited to achieve a high functionality. The proposed cell has the potential of implementing arbitrary local Boolean functions with n inputs. The cell has a complexity of only O(n) in the number of devices and template elements. For comparison, the digital n-to-1 multiplexor, a functionally equivalent system has a complexity of O(2n). A simple, piecewise-linear mathematical model is derived and used to evaluate the functional capabilities of the RTD-CNN cell. The model was proved to be accurate enough, and only minor tuning of some of the parameters is necessary to achieve the same functionality using a Spice simulation of the same circuit, which is based on more refined physical device models
Keywords :
Boolean functions; SPICE; cellular neural nets; computational complexity; neural chips; resonant tunnelling diodes; Boolean functions; Spice simulation; cellular neural network; complexity; multiple-nested universal cell; piecewise-linear mathematical model; resonant tunneling diodes; Boolean functions; Cellular neural networks; Computer networks; Laboratories; Logic gates; Logic testing; Piecewise linear techniques; RLC circuits; Resonant tunneling devices; Semiconductor diodes;
Conference_Titel :
Cellular Neural Networks and Their Applications, 2000. (CNNA 2000). Proceedings of the 2000 6th IEEE International Workshop on
Conference_Location :
Catania
Print_ISBN :
0-7803-6344-2
DOI :
10.1109/CNNA.2000.876842