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

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4 Author(s)
V. P. Roychowdhury ; School of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA ; D. B. Janes ; S. Bandyopadhyay ; Xiaodong Wang

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 100×100 pixel capacity, can be fabricated in an area of 10-8 cm2 leading to unprecedented functional density. Possible routes to synthesizing these circuits, employing self-assembly, are also discussed

Published in:

IEEE Transactions on Electron Devices  (Volume:43 ,  Issue: 10 )