The goal of this project is to carry out a detailed multi-disciplinary study of single-electron latching switches and of possible use of 2D arrays of such 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 10 8 neurons per cm 2 at manageable power dissipation below 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, not only provide complex information processing including complex image recognition, but possibly reproduce biological evolution of the cerebral cortex at a time scale some 6 orders of magnitude shorter. The objective of the proposed project is to carry out a preliminary study of this remarkable opportunity, addressing all its basic aspects at several structural levels. In particular, research will include the following components: A. Single-electron switch node design (D. Averin, K. Likharev, J. Wells). 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. B. Low temperature prototyping (J. Lukens). Fabrication and experimental study of Al/AlOx/Al prototypes of single-electron latching switches, with the goal to scale single-electron islands down to 100 nm and tunnel junctions to 10 nm, respectively, which would bring the reliable operation temperature up to about 10 K. C. Molecular single-electron device development (B. Brunschwig, J. Lukens, A. Mayr). Exploration of the opportunity to implement the basic component of the switches, the single-electron transistor, by chemical self-assembly of molecular components. The molecular components will be deposited in solution on the prefabricated metallic wire structures, and then characterized using a set of electrical, electrochemical, and time-resolved laser-spectrometry methods. D. Top level modeling and analysis (J. Barhen, M. Bender, K. Likharev). Large-scale computer simulation and a partial analytical study of the growth, dynamics, and self-adaptation of neuromorphic networks based on these switches. Hopefully, the project will achieve enough progress to justify a large-scale R&D effort in this exciting direction. In particular, a reliable evidence of self-organization of adaptive neuromorphic networks during largely unsupervised learning would certainly be followed by the first hardware implementations of sizable networks (possibly, after an initial stage of purely-CMOS-based prototyping using commercially available FPGA technology). The project will have a substantial educational component. Specifically (besides participating in general educational Stony Brook initiatives), at least 4 FTE graduate students will be involved in the project each year, and some 20 undergraduate and graduate students will take part in the project during its full 4-year period. At least one student will work in BNL and one in ORNL most of the time. Working in a multi-disciplinary team will allow these students to overcome inter-departmental barriers in their education. As another specific educational initiative, we plan to organize a Web-based undergraduate course on massively parallel supercomputing and neural networks, using the IBM SP3 computer at Oak Ridge. Work on the inter-related aspects of this multi-disciplinary project will be constantly coordinated by its P.I. (K. Likharev). In particular, regular meetings of all Stony Brook and Brookhaven participants of the team working on the project (including postdoctoral associates and students), and annual meetings with Oak Ridge collaborators, are planned.
