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Proceedings Paper

Feedforward, High Density, Programmable Read Only Neural Network Based Memory System
Author(s): Taher Daud; Alex Moopenn; James Lamb; Anil Thakoor; Satish Khanna
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Paper Abstract

Neural network models that mimic some functions of a brain use logic elements as neurons and connections of varying strength as synapses. Inspired by these models, we have developed a high density, programmable read only memory (PROM) that promises high-speed and fault tolerant capabilities. This memory is based on an innovative thin film implementation of binary synapses represented by a connection matrix with electronic amplifiers as input drivers and output sensors. A binary word is stored along a row of such a connection matrix of mutually perpendicular wires where the passive, two terminal, write-once synaptic interconnects consisting of a-Si:H microswitches are programmed to take on a value of '0' (synapse OFF) or a '1' (synapse ON). The retrieval of information is done either by addressing the matrix row-by-row or by content addressing in an associative recall. Thin film fabrication technology for this memory and the design options leading to very high density (up to a gigabit/cm2) PROM are described. Test circuits containing 40 x 40 binary synaptic arrays have been fabricated to study the a-Si:H memory switching properties for the 'write' operation. Results of the read operation and projections for larger memory blocks are discussed to highlight the potential for a read data rate exceeding 10 megabits/sec.

Paper Details

Date Published: 20 April 1988
PDF: 9 pages
Proc. SPIE 0880, High Speed Computing, (20 April 1988); doi: 10.1117/12.944039
Show Author Affiliations
Taher Daud, Jet Propulsion Laboratory (United States)
Alex Moopenn, Jet Propulsion Laboratory (United States)
James Lamb, Jet Propulsion Laboratory (United States)
Anil Thakoor, Jet Propulsion Laboratory (United States)
Satish Khanna, Jet Propulsion Laboratory (United States)

Published in SPIE Proceedings Vol. 0880:
High Speed Computing
David P. Casasent, Editor(s)

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