Collective computational activity in self-assembled arrays of quantum dots: a novel neuromorphic architecture for nanoelectronics

We describe a new class of nanoelectronic circuits which exploits the charging behavior in resistively/capacitively linked arrays of nanometer-sized metallic islands (quantum dots), self-assembled on a resonant tunneling diode, to perform neuromorphic computation. These circuits produce associative memory effects and realize the additive short-term memory (STM) or content addressable memory (CAM) models of neural networks without requiring either large-area/high-power operational amplifiers, or massive interconnectivity between devices. Both these requirements had seriously hindered the application of neural networks in the past. Additionally, the circuits can solve NP-complete optimization problems (such as the traveling salesman problem) using single electron charge dynamics, exhibit rudimentary image-processing capability, and operate at room temperature unlike most quantum devices. Two-dimensional (2D) processors, with a 100/spl times/100 pixel capacity, can be fabricated in an area of 10/sup -8/ cm/sup 2/ leading to unprecedented functional density. Possible routes to synthesizing these circuits, employing self-assembly, are also discussed.