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In this paper, a learnable cellular neural network (CNN) with space-variant templates and ratio memory (RM) called the RMCNN, is proposed and analyzed. By incorporating both modified Hebbian learning rule and RM into CNN architecture, the RMCNN as the associative memory can generate the absolute weights and then transform them into the ratioed A-template weights as the ratio memories for recognition of noisy input patterns. It is found from simulation results that due to the feature enhancement effect of RM, the RMCNN under constant leakage on template coefficients can store and recognize more patterns than the CNN associative memories without RM, but with the same learning rule and the same constant leakage on space-variant template coefficients. For 9 9 (18 18) RMCNNs, three (five) patterns can be learned, stored and recognized. Based upon the RMCNN architecture, an experimental chip of CMOS 9 9 RMCNN is designed and fabricated by using 0.35 m CMOS technology. The measurement results have successfully verified the correct functions of RMCNN.Index Terms-Cellular neural network (CNN), divider, multiplier, ratio memory (RM).

In this paper, the quantum-dot Largeneighborhood cellular neural (nonlinear) network (QLN-CNN) is proposed and analyzed. In the proposed QLN-CNN, the quantum dots are used to realized neuron cells whereas the strength of Coulombic forces among neurons are used as weights among neurons. The proposed QLN-CNN can perform the functions of image noise removal. It has small chip area and high cell density. Moreover, the power dissipation is very low. Thus large-size QLN-CNN could be realized for nanoelectronic systems. research efforts have been devoted to the implementation of CNNs using the QCA [ 3 ] . However, the LN-CNN realization using the QCA has not yet proposed so far. In this paper, the quantum-dot LN-CNN (QLN-CNN) is proposed and analyzed. In the proposed QLN-CNN, the QCA are used to form CNN neuron cells [4]. The Coulombic forces among neurons are used to realize the synaptic weights among neurons. The function of noise removal processing has been successfully performed in the QLN-CNN. This verifies the correct function of the proposed QLN-CNN. It has small chip area and low power dissipation, being suitable for large array application.

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