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A neural inspired lookup table for reconfigurable circuits is described and simulated. The design is based on conductive bridge RAM to implement the synapses and carbon nanotube field effect transistors (CNTFET) for the other parts. Electrical simulations demonstrate compatibility between the nanocomponents and show the successful training of a linearly separable logical function NOR3.
Inspec keywords: neural chips; logic gates; field effect transistors; random-access storage; table lookup; nanotube devices; carbon nanotubes; semiconductor device models; nanoelectronics; neural net architecture
Other keywords:
Subjects: Memory circuits; Fullerene, nanotube and related devices; Nanometre-scale semiconductor fabrication technology; Neural net devices; Insulated gate field effect transistors; Semiconductor storage; Logic elements; Logic circuits; Semiconductor device modelling, equivalent circuits, design and testing; Computer architecture; Neural nets (circuit implementations)