Analog VLSI and neural systems
Analog VLSI and neural systems
Scaling of MOS technology to submicrometer feature sizes
Journal of VLSI Signal Processing Systems - Joint special issue on Analog VLSI computation; also see Analog Integrated Circuits Signal Process., Vol. 6, No. 1
Analog Integrated Circuits and Signal Processing - Special issue: low-voltage low-power analog integrated circuits
Analog VLSI-based modeling of the primate oculomotor system
Neural Computation
Ultra Low-Voltage Floating-Gate (FGUVMOS) Amplifiers
Analog Integrated Circuits and Signal Processing - Special issue on selected papers from the NORCHIP 1999 conference
Adaptation of Current Signals with Floating-Gate Circuits
Analog Integrated Circuits and Signal Processing
ARVLSI '99 Proceedings of the 20th Anniversary Conference on Advanced Research in VLSI
Adaptive Circuits Using pFET Floating-Gate Devices
ARVLSI '99 Proceedings of the 20th Anniversary Conference on Advanced Research in VLSI
Adaptation of Current Signals with Floating-Gate Circuits
MICRONEURO '99 Proceedings of the 7th International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems
Neural networks with quantum gated nodes
Engineering Applications of Artificial Intelligence
Current-starved pseudo-floating gate amplifier
WSEAS Transactions on Circuits and Systems
NeuroNavigator: A Biologically Inspired Universal Cognitive Microcircuit
Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
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We have developed a complementary pair of pFETand nFET floating-gate silicon MOS transistors foranalog learning applications. The memory storage is nonvolatile;hot-electron injection and electron tunneling permit bidirectionalmemory updates. Because these updates depend on both the storedmemory value and the transistor terminal voltages, the synapsescan implement a learning function. We have derived a memory-updaterule for both devices, and have shown that the synapse learningfollows a simple power law. Unlike conventional EEPROMs, thesynapses allow simultaneous memory reading and writing. Synapsetransistor arrays can therefore compute both the array output,and local memory updates, in parallel. We have fabricated prototypesynaptic arrays; because the tunneling and injection processesare exponential in the transistor terminal voltages, the writeand erase isolation between array synapses is better than 0.01 percentThe synapses are small, and typically are operated at subthresholdcurrent levels; they will permit the development of dense, low-powersilicon learning systems.