In this project, we will carry out a detailed
theoretical study of single-electron latching switches
for hardware implementation of self-organizing
(“plastic”) neuromorphic networks. Preliminary
estimates show that such networks may provide
unparalleled possibilities for complex information
processing. By these estimates, the networks may
also have remarkable scaling properties: if
implemented using a 10-nm technology, they may
have density about 108 neurons per cm^2 at manageable
power dissipation about 100 W/cm^2, and feature full
learning cycle time of the order of a few seconds.
This scaling gives every hope that the networks will
be able, after initial (largely unsupervised) learning,
to not only provide complex information processing
including image recognition, but possibly reproduce
biological evolution of the cerebral cortex at a time
scale some 7 orders of magnitude shorter. The objective
of the proposed project is to carry out detailed
theoretical analysis and modeling (on two basic levels
of single-electron transport theory) of statics, dynamics,
and statistics of the proposed single-electron latching
switches. This project is part of a larger
collaborative effort, proposed to ONR and coordinated
through Prof. K.K. Likharev, SUNY Stony Brook, to
develop and characterize molecular single-electron
switches.
