Cellular nonlinear network based on semiconductor tunneling nanostructure

We propose and analyze a cellular nonlinear network (CNN) based on a semiconductor nanostructure consisting of multiple layers of two semiconductors along with an incorporated quantum dot layer. An elementary logic cell of the proposed CNN consists of two resonant tunneling diodes connected in series through a quantum dot. The cell may be realized with multiple layers of two semiconductor materials with an embedded dot layer in between. The local interconnections of nanocells are achieved via tunneling between the neighboring quantum dots. Cells may be biased by the common column contacts, and only edge cells have individual I/O ports. Using approximate tunneling characteristics, we simulated network dynamics and found procedures leading to useful logic functionality. In order to illustrate network capabilities for image processing, we present examples of filtering, erosion, dilation, and edge detection carried out on a test image on a 400/spl times/269 cell template. The realization of a number of logic functions in one module is possible due to the incorporation of nonlinear (tunneling) elements for cell interconnections. The proposed CNN architecture for nanostructures demonstrates powerful computing potential that will be beneficial for many practical applications.