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SUMMARYResonant tunnelling diodes (RTDs) have intriguing properties which make them a primary nanoelectronic device for both analogue and digital applications. We propose two di erent types of RTD-based cells for the cellular neural network (CNN) which exhibit superior performance in terms of complexity, functionality, or processing speed compared to standard cells. In the ÿrst cell model, the resistor of the standard cell is replaced by an RTD, which results in a more compact and versatile cell which requires neither self-feedback nor a non-linear output function, and allows three stable equilibrium points. If a resonant tunnelling transistor (RTT) is used instead of the RTD, the dynamics can be controlled through its gate voltage as an additional network parameter. In a majority of CNN applications, bistable cells are su cient. Utilizing RTD-based bistable logic elements to store the state of the cell, switching occurs almost instantaneously as virtually no charge transfer is necessary, and it is possible to implement non-linear connections in a straightforward manner. Hence, it turns out that RTD-based CNNs are tailor-made for the implementation of extremely fast bipolar operations and non-linear templates. The ideas presented in this paper may also be beneÿcially applied to other types of circuits and systems such as A=D converters or sigma-delta modulators.

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In this paper, we present an analytical design approach for the class of bipolar cellular neural networks (CNN's) which yields optimally robust template parameters. We give a rigorous definition of absolute and relative robustness and show that all well-defined CNN tasks are characterized by a finite set of linear and homogeneous inequalities. This system of inequalities can be analytically solved for the most robust template by simple matrix algebra. For the relative robustness of a task, a theoretical upper bound exists and is easily derived, whereas the absolute robustness can be arbitrarily increased by template scaling. A series of examples demonstrates the simplicity and broad applicability of the proposed method.Index Terms-Cellular neural networks (CNN's), robustness, template design.

A new simulator for Cellular Neural Networks (CNNs) is presented. In contrast to other simulators, the CNN cells are visualized in a grid structure, the values of input and states being represented by colors. Input and initial images can easily be generated and changed even while the integration of the system is in progress, and an oscilloscope function allows the quantitative study of CNN transients, thus providing insight into the dynamics of the network.For those who are new to the world of CNNs, a series of predefined templates set and demonstrations are available, which makes the simulator a valuable educational tool. Advanced users and CNN expert can examine manually-entered and parametrized templates and carry out experiments in a very broad spectrum of CNN theory and applications, including quantitative behavior, robustness aspects, settling time, state limitations, different output functions and numerical integration methods.The simulator is written in Java and publicly available on WWW and will run on any Web browser of the newer generations.