Molecular-junction-nanowire-crossbar-based neural network
A method for configuring nanoscale neural network circuits using molecular-junction-nanowire crossbars, and nanoscale neural networks produced by this method. Summing of weighted inputs within a neural-network node is implemented using variable-resistance resistors selectively configured at molecula...
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| creator | SNIDER GREG |
| description | A method for configuring nanoscale neural network circuits using molecular-junction-nanowire crossbars, and nanoscale neural networks produced by this method. Summing of weighted inputs within a neural-network node is implemented using variable-resistance resistors selectively configured at molecular-junction-nanowire-crossbar junctions. Thresholding functions for neural network nodes are implemented using pFET and nFET components selectively configured at molecular-junction-nanowire-crossbar junctions to provide an inverter. The output of one level of neural network nodes is directed, through selectively configured connections, to the resistor elements of a second level of neural network nodes via circuits created in the molecular-junction-nanowire crossbar. An arbitrary number of inputs, outputs, neural network node levels, nodes, weighting functions, and thresholding functions for any desired neural network are readily obtained by the methods of the present invention. |
| format | Patent |
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Summing of weighted inputs within a neural-network node is implemented using variable-resistance resistors selectively configured at molecular-junction-nanowire-crossbar junctions. Thresholding functions for neural network nodes are implemented using pFET and nFET components selectively configured at molecular-junction-nanowire-crossbar junctions to provide an inverter. The output of one level of neural network nodes is directed, through selectively configured connections, to the resistor elements of a second level of neural network nodes via circuits created in the molecular-junction-nanowire crossbar. An arbitrary number of inputs, outputs, neural network node levels, nodes, weighting functions, and thresholding functions for any desired neural network are readily obtained by the methods of the present invention.</description><language>eng</language><subject>ANALOGUE COMPUTERS ; BASIC ELECTRIC ELEMENTS ; CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR ; ELECTRICITY ; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC ; GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS ; INFORMATION STORAGE ; MANUFACTURE OR TREATMENT OF NANOSTRUCTURES ; MEASUREMENT OR ANALYSIS OF NANOSTRUCTURES ; NANOTECHNOLOGY ; OPTICAL COMPUTING DEVICES ; PERFORMING OPERATIONS ; PHYSICS ; SEMICONDUCTOR DEVICES ; SPECIFIC USES OR APPLICATIONS OF NANOSTRUCTURES ; STATIC STORES ; TECHNICAL SUBJECTS COVERED BY FORMER USPC ; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS ; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ARTCOLLECTIONS [XRACs] AND DIGESTS ; TRANSPORTING</subject><creationdate>2008</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20080415&DB=EPODOC&CC=US&NR=7359888B2$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20080415&DB=EPODOC&CC=US&NR=7359888B2$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>SNIDER GREG</creatorcontrib><title>Molecular-junction-nanowire-crossbar-based neural network</title><description>A method for configuring nanoscale neural network circuits using molecular-junction-nanowire crossbars, and nanoscale neural networks produced by this method. Summing of weighted inputs within a neural-network node is implemented using variable-resistance resistors selectively configured at molecular-junction-nanowire-crossbar junctions. Thresholding functions for neural network nodes are implemented using pFET and nFET components selectively configured at molecular-junction-nanowire-crossbar junctions to provide an inverter. The output of one level of neural network nodes is directed, through selectively configured connections, to the resistor elements of a second level of neural network nodes via circuits created in the molecular-junction-nanowire crossbar. An arbitrary number of inputs, outputs, neural network node levels, nodes, weighting functions, and thresholding functions for any desired neural network are readily obtained by the methods of the present invention.</description><subject>ANALOGUE COMPUTERS</subject><subject>BASIC ELECTRIC ELEMENTS</subject><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR</subject><subject>ELECTRICITY</subject><subject>GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC</subject><subject>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS</subject><subject>INFORMATION STORAGE</subject><subject>MANUFACTURE OR TREATMENT OF NANOSTRUCTURES</subject><subject>MEASUREMENT OR ANALYSIS OF NANOSTRUCTURES</subject><subject>NANOTECHNOLOGY</subject><subject>OPTICAL COMPUTING DEVICES</subject><subject>PERFORMING OPERATIONS</subject><subject>PHYSICS</subject><subject>SEMICONDUCTOR DEVICES</subject><subject>SPECIFIC USES OR APPLICATIONS OF NANOSTRUCTURES</subject><subject>STATIC STORES</subject><subject>TECHNICAL SUBJECTS COVERED BY FORMER USPC</subject><subject>TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS</subject><subject>TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ARTCOLLECTIONS [XRACs] AND DIGESTS</subject><subject>TRANSPORTING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2008</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLD0zc9JTS7NSSzSzSrNSy7JzM_TzUvMyy_PLErVTS7KLy5OAkolJRanpijkpZYWJeYAqZLy_KJsHgbWtMSc4lReKM3NoODmGuLsoZtakB-fWlyQmJwKVBkfGmxubGppYWHhZGRMhBIATKkvZg</recordid><startdate>20080415</startdate><enddate>20080415</enddate><creator>SNIDER GREG</creator><scope>EVB</scope></search><sort><creationdate>20080415</creationdate><title>Molecular-junction-nanowire-crossbar-based neural network</title><author>SNIDER GREG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US7359888B23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2008</creationdate><topic>ANALOGUE COMPUTERS</topic><topic>BASIC ELECTRIC ELEMENTS</topic><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR</topic><topic>ELECTRICITY</topic><topic>GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC</topic><topic>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS</topic><topic>INFORMATION STORAGE</topic><topic>MANUFACTURE OR TREATMENT OF NANOSTRUCTURES</topic><topic>MEASUREMENT OR ANALYSIS OF NANOSTRUCTURES</topic><topic>NANOTECHNOLOGY</topic><topic>OPTICAL COMPUTING DEVICES</topic><topic>PERFORMING OPERATIONS</topic><topic>PHYSICS</topic><topic>SEMICONDUCTOR DEVICES</topic><topic>SPECIFIC USES OR APPLICATIONS OF NANOSTRUCTURES</topic><topic>STATIC STORES</topic><topic>TECHNICAL SUBJECTS COVERED BY FORMER USPC</topic><topic>TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS</topic><topic>TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ARTCOLLECTIONS [XRACs] AND DIGESTS</topic><topic>TRANSPORTING</topic><toplevel>online_resources</toplevel><creatorcontrib>SNIDER GREG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>SNIDER GREG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Molecular-junction-nanowire-crossbar-based neural network</title><date>2008-04-15</date><risdate>2008</risdate><abstract>A method for configuring nanoscale neural network circuits using molecular-junction-nanowire crossbars, and nanoscale neural networks produced by this method. Summing of weighted inputs within a neural-network node is implemented using variable-resistance resistors selectively configured at molecular-junction-nanowire-crossbar junctions. Thresholding functions for neural network nodes are implemented using pFET and nFET components selectively configured at molecular-junction-nanowire-crossbar junctions to provide an inverter. The output of one level of neural network nodes is directed, through selectively configured connections, to the resistor elements of a second level of neural network nodes via circuits created in the molecular-junction-nanowire crossbar. An arbitrary number of inputs, outputs, neural network node levels, nodes, weighting functions, and thresholding functions for any desired neural network are readily obtained by the methods of the present invention.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | ANALOGUE COMPUTERS BASIC ELECTRIC ELEMENTS CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR ELECTRICITY GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS INFORMATION STORAGE MANUFACTURE OR TREATMENT OF NANOSTRUCTURES MEASUREMENT OR ANALYSIS OF NANOSTRUCTURES NANOTECHNOLOGY OPTICAL COMPUTING DEVICES PERFORMING OPERATIONS PHYSICS SEMICONDUCTOR DEVICES SPECIFIC USES OR APPLICATIONS OF NANOSTRUCTURES STATIC STORES TECHNICAL SUBJECTS COVERED BY FORMER USPC TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ARTCOLLECTIONS [XRACs] AND DIGESTS TRANSPORTING |
| title | Molecular-junction-nanowire-crossbar-based neural network |
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