The Effect of Aluminum Dopant Amount in Titania Film on the Memristor Electrical Properties
In a promising nanoelectronics device, namely, memristor based on metal oxides, there are many intermediate states with different conductivity between the limits of highly conductive and low-conducting states. These intermediate states can be used in the processes of associative learning of a neural...
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Veröffentlicht in: | Nano hybrids and composites 2020-02, Vol.28, p.59-64 |
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description | In a promising nanoelectronics device, namely, memristor based on metal oxides, there are many intermediate states with different conductivity between the limits of highly conductive and low-conducting states. These intermediate states can be used in the processes of associative learning of a neural network based on memristor synapses and simultaneous processing of input pulses, which consists in their weighing and summation in the neuroprocessor. By the method of simultaneous magnetron sputtering of two cathodes in a reactive oxygen environment, thin films of mixed oxides with a different mole ratio of titanium and aluminum were obtained. A method for obtaining a mixed oxide with a specified metal fractions by controlling the sputtering rates of cathodes using acoustic piezoelectric sensors is described. It is shown that the introduction of Al into titanium oxide improves the electrophysical characteristics of the memristor. The existence of an optimal fraction of Al dopant maximizing the memristor resistance ratio of the high-resistive and low-resistive states is established. The results indicate that the method of reactive magnetron deposition of mixed metal oxide by simultaneous sputtering of two cathodes provides a more uniform distribution of elements across the thickness of the active layer compared with the atomic layer deposition method. The uniform distribution is necessary to improve the stability of the memristor. It can be expected that in the memristors on mixed oxides TixSc1-xOy, HfxSc1-xOy, HfxY1-xOy, HfxLu1-xOy, ZrxSc1-xOy, ZrxY1-xOy, ZrxLu1-xOy an optimal dopant fraction corresponding to the maximally increased ratio of resistances in the high-resistance and low-resistance states will also be observed. Moreover, memristors on films with pure hafnium and zirconium oxides have a much larger range of resistive switching than titanium oxide. |
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These intermediate states can be used in the processes of associative learning of a neural network based on memristor synapses and simultaneous processing of input pulses, which consists in their weighing and summation in the neuroprocessor. By the method of simultaneous magnetron sputtering of two cathodes in a reactive oxygen environment, thin films of mixed oxides with a different mole ratio of titanium and aluminum were obtained. A method for obtaining a mixed oxide with a specified metal fractions by controlling the sputtering rates of cathodes using acoustic piezoelectric sensors is described. It is shown that the introduction of Al into titanium oxide improves the electrophysical characteristics of the memristor. The existence of an optimal fraction of Al dopant maximizing the memristor resistance ratio of the high-resistive and low-resistive states is established. The results indicate that the method of reactive magnetron deposition of mixed metal oxide by simultaneous sputtering of two cathodes provides a more uniform distribution of elements across the thickness of the active layer compared with the atomic layer deposition method. The uniform distribution is necessary to improve the stability of the memristor. It can be expected that in the memristors on mixed oxides TixSc1-xOy, HfxSc1-xOy, HfxY1-xOy, HfxLu1-xOy, ZrxSc1-xOy, ZrxY1-xOy, ZrxLu1-xOy an optimal dopant fraction corresponding to the maximally increased ratio of resistances in the high-resistance and low-resistance states will also be observed. Moreover, memristors on films with pure hafnium and zirconium oxides have a much larger range of resistive switching than titanium oxide.</description><identifier>ISSN: 2297-3370</identifier><identifier>ISSN: 2297-3400</identifier><identifier>EISSN: 2297-3400</identifier><identifier>DOI: 10.4028/www.scientific.net/NHC.28.59</identifier><language>eng</language><publisher>Zurich: Trans Tech Publications Ltd</publisher><subject>Aluminum ; Atomic layer epitaxy ; Cathode sputtering ; Dopants ; Electrical properties ; Electrical resistivity ; Magnetron sputtering ; Memristors ; Metal oxides ; Mixed oxides ; Nanoelectronics ; Nanotechnology devices ; Neural networks ; Optimization ; Piezoelectricity ; Synapses ; Thickness ; Thin films ; Titanium ; Titanium dioxide ; Titanium oxides ; Zirconium oxides</subject><ispartof>Nano hybrids and composites, 2020-02, Vol.28, p.59-64</ispartof><rights>2020 Trans Tech Publications Ltd</rights><rights>Copyright Trans Tech Publications Ltd. Feb 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c280t-d776b6604d3a25f14e25b7b5390134e4a4e282b68ec222bf67a3d6ba8956fea83</citedby><cites>FETCH-LOGICAL-c280t-d776b6604d3a25f14e25b7b5390134e4a4e282b68ec222bf67a3d6ba8956fea83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttps://www.scientific.net/Image/TitleCover/6025?width=600</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Bobylev, Andrey Nikolayevich</creatorcontrib><creatorcontrib>Busygin, Alexander Nikolayevich</creatorcontrib><creatorcontrib>Ebrahim, Abdullah Haidar</creatorcontrib><creatorcontrib>Udovichenko, Sergey Yurievich</creatorcontrib><title>The Effect of Aluminum Dopant Amount in Titania Film on the Memristor Electrical Properties</title><title>Nano hybrids and composites</title><description>In a promising nanoelectronics device, namely, memristor based on metal oxides, there are many intermediate states with different conductivity between the limits of highly conductive and low-conducting states. These intermediate states can be used in the processes of associative learning of a neural network based on memristor synapses and simultaneous processing of input pulses, which consists in their weighing and summation in the neuroprocessor. By the method of simultaneous magnetron sputtering of two cathodes in a reactive oxygen environment, thin films of mixed oxides with a different mole ratio of titanium and aluminum were obtained. A method for obtaining a mixed oxide with a specified metal fractions by controlling the sputtering rates of cathodes using acoustic piezoelectric sensors is described. It is shown that the introduction of Al into titanium oxide improves the electrophysical characteristics of the memristor. The existence of an optimal fraction of Al dopant maximizing the memristor resistance ratio of the high-resistive and low-resistive states is established. The results indicate that the method of reactive magnetron deposition of mixed metal oxide by simultaneous sputtering of two cathodes provides a more uniform distribution of elements across the thickness of the active layer compared with the atomic layer deposition method. The uniform distribution is necessary to improve the stability of the memristor. It can be expected that in the memristors on mixed oxides TixSc1-xOy, HfxSc1-xOy, HfxY1-xOy, HfxLu1-xOy, ZrxSc1-xOy, ZrxY1-xOy, ZrxLu1-xOy an optimal dopant fraction corresponding to the maximally increased ratio of resistances in the high-resistance and low-resistance states will also be observed. Moreover, memristors on films with pure hafnium and zirconium oxides have a much larger range of resistive switching than titanium oxide.</description><subject>Aluminum</subject><subject>Atomic layer epitaxy</subject><subject>Cathode sputtering</subject><subject>Dopants</subject><subject>Electrical properties</subject><subject>Electrical resistivity</subject><subject>Magnetron sputtering</subject><subject>Memristors</subject><subject>Metal oxides</subject><subject>Mixed oxides</subject><subject>Nanoelectronics</subject><subject>Nanotechnology devices</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Piezoelectricity</subject><subject>Synapses</subject><subject>Thickness</subject><subject>Thin films</subject><subject>Titanium</subject><subject>Titanium dioxide</subject><subject>Titanium oxides</subject><subject>Zirconium oxides</subject><issn>2297-3370</issn><issn>2297-3400</issn><issn>2297-3400</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNqNkF1LwzAUhoMoOOb-Q0Bv26Vpm6YgwpibE-bHxbzyIqRZwjLapCYpY__eyJTdenUO57wf8ABwl6G0QJhOD4dD6oWWJmilRWpkmL6u5immaVlfgBHGdZXkBUKXf3teoWsw8X6PEMJZVmcEj8DnZifhQikpArQKztqh02bo4KPtuQlw1tkhDm3gRgduNIdL3XbQGhii70V2TvtgHVy0McBpwVv47mwvXdDS34ArxVsvJ79zDD6Wi818lazfnp7ns3UiMEUh2VYVaQhBxTbnuFRZIXHZVE2Z1yjLC1nweKC4IVQKjHGjSMXzLWk4rUuiJKf5GNyecntnvwbpA9vbwZlYyXAEQGlVlCSq7k8q4az3TirWO91xd2QZYj9EWSTKzkRZJMoiURY_ZR3tDyd7cNz4IMXu3PKvgG8-gYa6</recordid><startdate>20200227</startdate><enddate>20200227</enddate><creator>Bobylev, Andrey Nikolayevich</creator><creator>Busygin, Alexander Nikolayevich</creator><creator>Ebrahim, Abdullah Haidar</creator><creator>Udovichenko, Sergey Yurievich</creator><general>Trans Tech Publications Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BFMQW</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>L7M</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20200227</creationdate><title>The Effect of Aluminum Dopant Amount in Titania Film on the Memristor Electrical Properties</title><author>Bobylev, Andrey Nikolayevich ; Busygin, Alexander Nikolayevich ; Ebrahim, Abdullah Haidar ; Udovichenko, Sergey Yurievich</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c280t-d776b6604d3a25f14e25b7b5390134e4a4e282b68ec222bf67a3d6ba8956fea83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Aluminum</topic><topic>Atomic layer epitaxy</topic><topic>Cathode sputtering</topic><topic>Dopants</topic><topic>Electrical properties</topic><topic>Electrical resistivity</topic><topic>Magnetron sputtering</topic><topic>Memristors</topic><topic>Metal oxides</topic><topic>Mixed oxides</topic><topic>Nanoelectronics</topic><topic>Nanotechnology devices</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Piezoelectricity</topic><topic>Synapses</topic><topic>Thickness</topic><topic>Thin films</topic><topic>Titanium</topic><topic>Titanium dioxide</topic><topic>Titanium oxides</topic><topic>Zirconium oxides</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bobylev, Andrey Nikolayevich</creatorcontrib><creatorcontrib>Busygin, Alexander Nikolayevich</creatorcontrib><creatorcontrib>Ebrahim, Abdullah Haidar</creatorcontrib><creatorcontrib>Udovichenko, Sergey Yurievich</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Continental Europe Database</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Nano hybrids and composites</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bobylev, Andrey Nikolayevich</au><au>Busygin, Alexander Nikolayevich</au><au>Ebrahim, Abdullah Haidar</au><au>Udovichenko, Sergey Yurievich</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Effect of Aluminum Dopant Amount in Titania Film on the Memristor Electrical Properties</atitle><jtitle>Nano hybrids and composites</jtitle><date>2020-02-27</date><risdate>2020</risdate><volume>28</volume><spage>59</spage><epage>64</epage><pages>59-64</pages><issn>2297-3370</issn><issn>2297-3400</issn><eissn>2297-3400</eissn><abstract>In a promising nanoelectronics device, namely, memristor based on metal oxides, there are many intermediate states with different conductivity between the limits of highly conductive and low-conducting states. These intermediate states can be used in the processes of associative learning of a neural network based on memristor synapses and simultaneous processing of input pulses, which consists in their weighing and summation in the neuroprocessor. By the method of simultaneous magnetron sputtering of two cathodes in a reactive oxygen environment, thin films of mixed oxides with a different mole ratio of titanium and aluminum were obtained. A method for obtaining a mixed oxide with a specified metal fractions by controlling the sputtering rates of cathodes using acoustic piezoelectric sensors is described. It is shown that the introduction of Al into titanium oxide improves the electrophysical characteristics of the memristor. The existence of an optimal fraction of Al dopant maximizing the memristor resistance ratio of the high-resistive and low-resistive states is established. The results indicate that the method of reactive magnetron deposition of mixed metal oxide by simultaneous sputtering of two cathodes provides a more uniform distribution of elements across the thickness of the active layer compared with the atomic layer deposition method. The uniform distribution is necessary to improve the stability of the memristor. It can be expected that in the memristors on mixed oxides TixSc1-xOy, HfxSc1-xOy, HfxY1-xOy, HfxLu1-xOy, ZrxSc1-xOy, ZrxY1-xOy, ZrxLu1-xOy an optimal dopant fraction corresponding to the maximally increased ratio of resistances in the high-resistance and low-resistance states will also be observed. Moreover, memristors on films with pure hafnium and zirconium oxides have a much larger range of resistive switching than titanium oxide.</abstract><cop>Zurich</cop><pub>Trans Tech Publications Ltd</pub><doi>10.4028/www.scientific.net/NHC.28.59</doi><tpages>6</tpages></addata></record> |
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subjects | Aluminum Atomic layer epitaxy Cathode sputtering Dopants Electrical properties Electrical resistivity Magnetron sputtering Memristors Metal oxides Mixed oxides Nanoelectronics Nanotechnology devices Neural networks Optimization Piezoelectricity Synapses Thickness Thin films Titanium Titanium dioxide Titanium oxides Zirconium oxides |
title | The Effect of Aluminum Dopant Amount in Titania Film on the Memristor Electrical Properties |
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