Image Processing by a Programmable Grid Comprising Quantum Dots and Memristors

Real-time vision systems require computationally intensive tasks which often benefit greatly from fast and accurate feature extractions. Resistive grid-based analog structures have been shown to perform these tasks with high accuracy and added advantages of compact area, noise immunity, and lower power consumption compared to their digital counterparts. However, these are static structures and can only perform one type of image processing task. In this paper, an analog programmable memristive grid-based architecture capable of performing various real-time image processing tasks such as edge and line detections is presented. The unit cell structure employs 3-D confined resonant tunneling diodes that are called quantum dots in this paper for signal amplification and latching, and these dots are interconnected between neighboring cells through nonvolatile continuously variable resistive elements that are more popularly known as memristors. A method to program memristive connections is introduced and verified through circuit simulations. Various diffusion characteristics, edge detection, and line detection tasks have been demonstrated through simulations using a 2-D array of the proposed cell structure and analytical models have been provided.