Research presented in this thesis provides
a substantial leap from the study of interesting
device physics to fully adaptive analog networks
and lays a solid foundation for future development
of large-scale, compact, low-power adaptive parallel
analog computation systems.
The investigation described here started with
observation of this potential learning capability
and led to the first derivation and characterization of
the floating-gate pFET correlation learning rule.
Starting with two synapses sharing the same error signal,
we progressed from phase correlation experiments
through correlation experiments involving harmonically related sinusoids,
culminating in learning the Fourier series coefficients
of a square wave cite{kn:Dugger2000}.
Extending these earlier two-input node experiments to the general case
of correlated inputs required dealing with
weight decay naturally exhibited by the learning rule.
We introduced a source-follower floating-gate synapse
as an improvement over our earlier source-degenerated floating-gate synapse
in terms of relative weight decay cite{kn:Dugger2004}.
A larger network of source-follower floating-gate synapses was fabricated
and an FPGA-controlled testboard was designed and built.
This more sophisticated system provides an excellent
framework for exploring applications to multi-input, multi-node
adaptive filtering applications.
Adaptive channel equalization provided
a practical test-case illustrating the use
of these adaptive systems in solving real-world problems.
The same system could easily be applied to noise and echo cancellation
in communication systems and system identification tasks in
optimal control problems.
We envision the commercialization of these adaptive analog VLSI
systems as practical products within a couple of years.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/5294 |
| Date | 26 November 2003 |
| Creators | Dugger, Jeffery Don |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 1137791 bytes, application/pdf |
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