Abstract
The resonant tunneling diodes (RTD) have found numerous applications in high-speed digital and analog circuits owing to its folded-back negative differential resistance (NDR) in current-voltage (I-V) characteristics and nanometer size. On account of the replacement of the state resistor in standard cell by an RTD, an RTD-based cellular neural/nonlinear network (RTD-CNN) can be obtained, in which the cell requires neither self-feedback nor a nonlinear output, thereby being more compact and versatile. This paper addresses the structure of RTD-CNN in detail and investigates its fault-tolerant properties in image processing taking horizontal line detection and edge extraction, for examples. A series of computer simulations demonstrates the promising fault-tolerant abilities of the RTD-CNN.
Highlights
In 1988, Chua and Yang defined cellular neural/nonlinear network (CNN) based on cellular automata and neural network [1, 2]
The resonant tunneling diodes (RTD) have found numerous applications in high-speed digital and analog circuits owing to its folded-back negative differential resistance (NDR) in current-voltage (I-V) characteristics and nanometer size
This paper investigates the resonant tunneling diodes based cellular neural/nonlinear networks (RTD-CNN) with fault-tolerant properties in image processing
Summary
In 1988, Chua and Yang defined cellular neural/nonlinear network (CNN) based on cellular automata and neural network [1, 2]. CNN was acclaimed as a powerful back-end analog array processor and capable of accelerating various computation intensive tasks in image processing, motion detection, pattern formation and recognition, and robotics [3]. The structure of this network is easy to implement in very large integration (VLSI) technologies. Among several proposed nanometer electronic devices, the resonant tunneling diode possesses a relatively easy fabrication process and folded-back negative differential resistance (NDR) in current-voltage (I-V) characteristics [5, 6], thereby exploring several applications in both digital and analog circuits [7]. This paper investigates the resonant tunneling diodes based cellular neural/nonlinear networks (RTD-CNN) with fault-tolerant properties in image processing.
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