NOTE: Text or symbols not renderable in plain ASCII are indicated by [...]. Abstract is included in .pdf document. Floating-gate technology can be used to build silicon systems that adapt and learn. This technology is well suited to implement adaptation and learning because we are not building analog EEPROMS, but rather circuit elements with important time domain dynamics. These floating-gate circuits use the hot-electron-injection, electron-tunneling, and drain-induced-barrier-lowering phenomena in a standard submicron CMOS process. This technology works with the constraints of the silicon medium, and is similar to biological systems that turned potential liabilities into features. I develop the first analytical model of the impact-i...
A computation is an operation that can be performed by a physical machine. We are familiar with digi...
Journal ArticleThe exponential behavior of MOSFETs in subthreshold operation has recently been expl...
Synapses plays an important role of learning in a neural network; the learning rules which modify th...
We have developed a new floating-gate silicon MOS transistor for analog learning applications. The m...
Our goal is to develop silicon learning systems. One impediment to achieving this goal has been the ...
We have developed a complementary pair of pFET and nFET floating-gate silicon MOS transistors for an...
Research presented in this thesis provides a substantial leap from the study of interesting devi...
In this work, programmable analog techniques using floating-gate transistors have been developed to ...
We describe single-transistor silicon synapses that compute, learn, and provide non-volatile memory ...
We describe single-transistor silicon synapses that compute, learn, and provide non-volatile memory ...
We apply adaptation into ordinary circuits and systems to achieve high performance, high quality res...
We have demonstrated on-chip learning in an array of floating-gate MOS synapse transistors. The arra...
Analog floating-gate transistors enable complex analog processing to be implemented in VLSI circuits...
We have developed an amplifier which removes its “off-set” as a natural part of its operation by mod...
fabrication of silicon versions of artificial neural learning algorithms in existing VLSI processes ...
A computation is an operation that can be performed by a physical machine. We are familiar with digi...
Journal ArticleThe exponential behavior of MOSFETs in subthreshold operation has recently been expl...
Synapses plays an important role of learning in a neural network; the learning rules which modify th...
We have developed a new floating-gate silicon MOS transistor for analog learning applications. The m...
Our goal is to develop silicon learning systems. One impediment to achieving this goal has been the ...
We have developed a complementary pair of pFET and nFET floating-gate silicon MOS transistors for an...
Research presented in this thesis provides a substantial leap from the study of interesting devi...
In this work, programmable analog techniques using floating-gate transistors have been developed to ...
We describe single-transistor silicon synapses that compute, learn, and provide non-volatile memory ...
We describe single-transistor silicon synapses that compute, learn, and provide non-volatile memory ...
We apply adaptation into ordinary circuits and systems to achieve high performance, high quality res...
We have demonstrated on-chip learning in an array of floating-gate MOS synapse transistors. The arra...
Analog floating-gate transistors enable complex analog processing to be implemented in VLSI circuits...
We have developed an amplifier which removes its “off-set” as a natural part of its operation by mod...
fabrication of silicon versions of artificial neural learning algorithms in existing VLSI processes ...
A computation is an operation that can be performed by a physical machine. We are familiar with digi...
Journal ArticleThe exponential behavior of MOSFETs in subthreshold operation has recently been expl...
Synapses plays an important role of learning in a neural network; the learning rules which modify th...