Computational paradigm for nanoelectronics: self-assembled quantum dot cellular neural networks

Recent work on a unique locally interconnected neuromorphic architecture that can be implemented with chemically self-assembled arrays of nanowires acting as circuit nodes is reviewed. The nanowires have non-monotonic and nonlinear current/voltage characteristics (e.g. a negative differential resistance) that provide the functionality needed for complex circuit functions. This self-assembled network, which in its most rudimentary form can be ´grown´ in a beaker using traditional electrochemistry, is theoretically capable of performing Boolean logic operations, complex image processing tasks, and associative memory functions. Relevant features of the network are described, and some recent results are presented, in particular, experimental results showing that the transport nonlinearities of the nanowires can be modulated with infrared radiation. This makes it naturally amenable to optical inputs which eliminates the need for electrical input connections and contacts, thereby allowing extremely high device density. It also makes it eminently suitable for image processing tasks. Furthermore, critical features of the system are synergistic with biologically inspired networks. The relevant circuit parameters have been experimentally extracted using a prototype self-assembled structure, and they have been used in simulations to demonstrate functionality of the network for several applications.