A SPICE compact model for forming-free, low-power graphene-insulator-graphene ReRAM technology
Abstract Development of scalable, low-power resistive memory devices (ReRAM) can be crucial for energy efficient neural networks with enhanced compute-in-memory capability. Recent demonstrations show promise for graphene as an electrode material for ultra-low power switching in ReRAMs. However, a li...
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| Autor*in: |
Reddy, L. Harshit [verfasserIn] Pande, Shubham R. [verfasserIn] Roy, Tania [verfasserIn] Vogel, Eric M. [verfasserIn] Chakravorty, Anjan [verfasserIn] Chakrabarti, Bhaswar [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© Qatar University and Springer Nature Switzerland AG 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Emergent materials - Cham : Springer International Publishing, 2018, 4(2021), 4 vom: 30. Juli, Seite 1055-1065 |
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| Übergeordnetes Werk: |
volume:4 ; year:2021 ; number:4 ; day:30 ; month:07 ; pages:1055-1065 |
| Links: |
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| DOI / URN: |
10.1007/s42247-021-00265-8 |
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| Katalog-ID: |
SPR044831021 |
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| 520 | |a Abstract Development of scalable, low-power resistive memory devices (ReRAM) can be crucial for energy efficient neural networks with enhanced compute-in-memory capability. Recent demonstrations show promise for graphene as an electrode material for ultra-low power switching in ReRAMs. However, a limited amount of research has been carried out towards developing a SPICE-based compact model that captures the switching dynamics of such devices. In this work, we investigate a low-power, forming-free resistive memory device with graphene electrodes and a multi-layered $ TiO_{x} $/$ Al_{2} %$ O_{3} $/$ TiO_{2} $ dielectric stack. We first develop a compact model to demonstrate that the switching dynamics can be simulated by considering permanent conductive filaments in the $ TiO_{x} $ and $ TiO_{2} $ layers and by the modulation of a tunnelling gap within the $ Al_{2} %$ O_{3} $ layer. The developed devices also exhibit strong rectification behavior in the ON state. We incorporate this rectification behavior in the developed compact model. We also demonstrate that multiple filaments govern the switching dynamics at higher operating current values. The developed model also accurately captures the stochastic variability experimentally observed in the ReRAM devices. This work shows promise for simulation of large-scale networks of graphene-based low-power ReRAM technology. | ||
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| 700 | 1 | |a Pande, Shubham R. |e verfasserin |4 aut | |
| 700 | 1 | |a Roy, Tania |e verfasserin |4 aut | |
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| 700 | 1 | |a Chakravorty, Anjan |e verfasserin |4 aut | |
| 700 | 1 | |a Chakrabarti, Bhaswar |e verfasserin |4 aut | |
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10.1007/s42247-021-00265-8 doi (DE-627)SPR044831021 (SPR)s42247-021-00265-8-e DE-627 ger DE-627 rakwb eng 530 540 ASE 530 540 ASE Reddy, L. Harshit verfasserin aut A SPICE compact model for forming-free, low-power graphene-insulator-graphene ReRAM technology 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Qatar University and Springer Nature Switzerland AG 2021 Abstract Development of scalable, low-power resistive memory devices (ReRAM) can be crucial for energy efficient neural networks with enhanced compute-in-memory capability. Recent demonstrations show promise for graphene as an electrode material for ultra-low power switching in ReRAMs. However, a limited amount of research has been carried out towards developing a SPICE-based compact model that captures the switching dynamics of such devices. In this work, we investigate a low-power, forming-free resistive memory device with graphene electrodes and a multi-layered $ TiO_{x} $/$ Al_{2} %$ O_{3} $/$ TiO_{2} $ dielectric stack. We first develop a compact model to demonstrate that the switching dynamics can be simulated by considering permanent conductive filaments in the $ TiO_{x} $ and $ TiO_{2} $ layers and by the modulation of a tunnelling gap within the $ Al_{2} %$ O_{3} $ layer. The developed devices also exhibit strong rectification behavior in the ON state. We incorporate this rectification behavior in the developed compact model. We also demonstrate that multiple filaments govern the switching dynamics at higher operating current values. The developed model also accurately captures the stochastic variability experimentally observed in the ReRAM devices. This work shows promise for simulation of large-scale networks of graphene-based low-power ReRAM technology. ReRAM (dpeaa)DE-He213 Graphene (dpeaa)DE-He213 Conductive filament (dpeaa)DE-He213 Rectification (dpeaa)DE-He213 Compact model (dpeaa)DE-He213 Pande, Shubham R. verfasserin aut Roy, Tania verfasserin aut Vogel, Eric M. verfasserin aut Chakravorty, Anjan verfasserin aut Chakrabarti, Bhaswar verfasserin aut Enthalten in Emergent materials Cham : Springer International Publishing, 2018 4(2021), 4 vom: 30. Juli, Seite 1055-1065 (DE-627)1030851352 (DE-600)2942631-5 2522-574X nnns volume:4 year:2021 number:4 day:30 month:07 pages:1055-1065 https://dx.doi.org/10.1007/s42247-021-00265-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 4 30 07 1055-1065 |
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10.1007/s42247-021-00265-8 doi (DE-627)SPR044831021 (SPR)s42247-021-00265-8-e DE-627 ger DE-627 rakwb eng 530 540 ASE 530 540 ASE Reddy, L. Harshit verfasserin aut A SPICE compact model for forming-free, low-power graphene-insulator-graphene ReRAM technology 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Qatar University and Springer Nature Switzerland AG 2021 Abstract Development of scalable, low-power resistive memory devices (ReRAM) can be crucial for energy efficient neural networks with enhanced compute-in-memory capability. Recent demonstrations show promise for graphene as an electrode material for ultra-low power switching in ReRAMs. However, a limited amount of research has been carried out towards developing a SPICE-based compact model that captures the switching dynamics of such devices. In this work, we investigate a low-power, forming-free resistive memory device with graphene electrodes and a multi-layered $ TiO_{x} $/$ Al_{2} %$ O_{3} $/$ TiO_{2} $ dielectric stack. We first develop a compact model to demonstrate that the switching dynamics can be simulated by considering permanent conductive filaments in the $ TiO_{x} $ and $ TiO_{2} $ layers and by the modulation of a tunnelling gap within the $ Al_{2} %$ O_{3} $ layer. The developed devices also exhibit strong rectification behavior in the ON state. We incorporate this rectification behavior in the developed compact model. We also demonstrate that multiple filaments govern the switching dynamics at higher operating current values. The developed model also accurately captures the stochastic variability experimentally observed in the ReRAM devices. This work shows promise for simulation of large-scale networks of graphene-based low-power ReRAM technology. ReRAM (dpeaa)DE-He213 Graphene (dpeaa)DE-He213 Conductive filament (dpeaa)DE-He213 Rectification (dpeaa)DE-He213 Compact model (dpeaa)DE-He213 Pande, Shubham R. verfasserin aut Roy, Tania verfasserin aut Vogel, Eric M. verfasserin aut Chakravorty, Anjan verfasserin aut Chakrabarti, Bhaswar verfasserin aut Enthalten in Emergent materials Cham : Springer International Publishing, 2018 4(2021), 4 vom: 30. Juli, Seite 1055-1065 (DE-627)1030851352 (DE-600)2942631-5 2522-574X nnns volume:4 year:2021 number:4 day:30 month:07 pages:1055-1065 https://dx.doi.org/10.1007/s42247-021-00265-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 4 30 07 1055-1065 |
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10.1007/s42247-021-00265-8 doi (DE-627)SPR044831021 (SPR)s42247-021-00265-8-e DE-627 ger DE-627 rakwb eng 530 540 ASE 530 540 ASE Reddy, L. Harshit verfasserin aut A SPICE compact model for forming-free, low-power graphene-insulator-graphene ReRAM technology 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Qatar University and Springer Nature Switzerland AG 2021 Abstract Development of scalable, low-power resistive memory devices (ReRAM) can be crucial for energy efficient neural networks with enhanced compute-in-memory capability. Recent demonstrations show promise for graphene as an electrode material for ultra-low power switching in ReRAMs. However, a limited amount of research has been carried out towards developing a SPICE-based compact model that captures the switching dynamics of such devices. In this work, we investigate a low-power, forming-free resistive memory device with graphene electrodes and a multi-layered $ TiO_{x} $/$ Al_{2} %$ O_{3} $/$ TiO_{2} $ dielectric stack. We first develop a compact model to demonstrate that the switching dynamics can be simulated by considering permanent conductive filaments in the $ TiO_{x} $ and $ TiO_{2} $ layers and by the modulation of a tunnelling gap within the $ Al_{2} %$ O_{3} $ layer. The developed devices also exhibit strong rectification behavior in the ON state. We incorporate this rectification behavior in the developed compact model. We also demonstrate that multiple filaments govern the switching dynamics at higher operating current values. The developed model also accurately captures the stochastic variability experimentally observed in the ReRAM devices. This work shows promise for simulation of large-scale networks of graphene-based low-power ReRAM technology. ReRAM (dpeaa)DE-He213 Graphene (dpeaa)DE-He213 Conductive filament (dpeaa)DE-He213 Rectification (dpeaa)DE-He213 Compact model (dpeaa)DE-He213 Pande, Shubham R. verfasserin aut Roy, Tania verfasserin aut Vogel, Eric M. verfasserin aut Chakravorty, Anjan verfasserin aut Chakrabarti, Bhaswar verfasserin aut Enthalten in Emergent materials Cham : Springer International Publishing, 2018 4(2021), 4 vom: 30. Juli, Seite 1055-1065 (DE-627)1030851352 (DE-600)2942631-5 2522-574X nnns volume:4 year:2021 number:4 day:30 month:07 pages:1055-1065 https://dx.doi.org/10.1007/s42247-021-00265-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 4 30 07 1055-1065 |
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10.1007/s42247-021-00265-8 doi (DE-627)SPR044831021 (SPR)s42247-021-00265-8-e DE-627 ger DE-627 rakwb eng 530 540 ASE 530 540 ASE Reddy, L. Harshit verfasserin aut A SPICE compact model for forming-free, low-power graphene-insulator-graphene ReRAM technology 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Qatar University and Springer Nature Switzerland AG 2021 Abstract Development of scalable, low-power resistive memory devices (ReRAM) can be crucial for energy efficient neural networks with enhanced compute-in-memory capability. Recent demonstrations show promise for graphene as an electrode material for ultra-low power switching in ReRAMs. However, a limited amount of research has been carried out towards developing a SPICE-based compact model that captures the switching dynamics of such devices. In this work, we investigate a low-power, forming-free resistive memory device with graphene electrodes and a multi-layered $ TiO_{x} $/$ Al_{2} %$ O_{3} $/$ TiO_{2} $ dielectric stack. We first develop a compact model to demonstrate that the switching dynamics can be simulated by considering permanent conductive filaments in the $ TiO_{x} $ and $ TiO_{2} $ layers and by the modulation of a tunnelling gap within the $ Al_{2} %$ O_{3} $ layer. The developed devices also exhibit strong rectification behavior in the ON state. We incorporate this rectification behavior in the developed compact model. We also demonstrate that multiple filaments govern the switching dynamics at higher operating current values. The developed model also accurately captures the stochastic variability experimentally observed in the ReRAM devices. This work shows promise for simulation of large-scale networks of graphene-based low-power ReRAM technology. ReRAM (dpeaa)DE-He213 Graphene (dpeaa)DE-He213 Conductive filament (dpeaa)DE-He213 Rectification (dpeaa)DE-He213 Compact model (dpeaa)DE-He213 Pande, Shubham R. verfasserin aut Roy, Tania verfasserin aut Vogel, Eric M. verfasserin aut Chakravorty, Anjan verfasserin aut Chakrabarti, Bhaswar verfasserin aut Enthalten in Emergent materials Cham : Springer International Publishing, 2018 4(2021), 4 vom: 30. Juli, Seite 1055-1065 (DE-627)1030851352 (DE-600)2942631-5 2522-574X nnns volume:4 year:2021 number:4 day:30 month:07 pages:1055-1065 https://dx.doi.org/10.1007/s42247-021-00265-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 4 30 07 1055-1065 |
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10.1007/s42247-021-00265-8 doi (DE-627)SPR044831021 (SPR)s42247-021-00265-8-e DE-627 ger DE-627 rakwb eng 530 540 ASE 530 540 ASE Reddy, L. Harshit verfasserin aut A SPICE compact model for forming-free, low-power graphene-insulator-graphene ReRAM technology 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Qatar University and Springer Nature Switzerland AG 2021 Abstract Development of scalable, low-power resistive memory devices (ReRAM) can be crucial for energy efficient neural networks with enhanced compute-in-memory capability. Recent demonstrations show promise for graphene as an electrode material for ultra-low power switching in ReRAMs. However, a limited amount of research has been carried out towards developing a SPICE-based compact model that captures the switching dynamics of such devices. In this work, we investigate a low-power, forming-free resistive memory device with graphene electrodes and a multi-layered $ TiO_{x} $/$ Al_{2} %$ O_{3} $/$ TiO_{2} $ dielectric stack. We first develop a compact model to demonstrate that the switching dynamics can be simulated by considering permanent conductive filaments in the $ TiO_{x} $ and $ TiO_{2} $ layers and by the modulation of a tunnelling gap within the $ Al_{2} %$ O_{3} $ layer. The developed devices also exhibit strong rectification behavior in the ON state. We incorporate this rectification behavior in the developed compact model. We also demonstrate that multiple filaments govern the switching dynamics at higher operating current values. The developed model also accurately captures the stochastic variability experimentally observed in the ReRAM devices. This work shows promise for simulation of large-scale networks of graphene-based low-power ReRAM technology. ReRAM (dpeaa)DE-He213 Graphene (dpeaa)DE-He213 Conductive filament (dpeaa)DE-He213 Rectification (dpeaa)DE-He213 Compact model (dpeaa)DE-He213 Pande, Shubham R. verfasserin aut Roy, Tania verfasserin aut Vogel, Eric M. verfasserin aut Chakravorty, Anjan verfasserin aut Chakrabarti, Bhaswar verfasserin aut Enthalten in Emergent materials Cham : Springer International Publishing, 2018 4(2021), 4 vom: 30. Juli, Seite 1055-1065 (DE-627)1030851352 (DE-600)2942631-5 2522-574X nnns volume:4 year:2021 number:4 day:30 month:07 pages:1055-1065 https://dx.doi.org/10.1007/s42247-021-00265-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 4 30 07 1055-1065 |
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Reddy, L. Harshit @@aut@@ Pande, Shubham R. @@aut@@ Roy, Tania @@aut@@ Vogel, Eric M. @@aut@@ Chakravorty, Anjan @@aut@@ Chakrabarti, Bhaswar @@aut@@ |
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Harshit</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A SPICE compact model for forming-free, low-power graphene-insulator-graphene ReRAM technology</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Qatar University and Springer Nature Switzerland AG 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Development of scalable, low-power resistive memory devices (ReRAM) can be crucial for energy efficient neural networks with enhanced compute-in-memory capability. Recent demonstrations show promise for graphene as an electrode material for ultra-low power switching in ReRAMs. However, a limited amount of research has been carried out towards developing a SPICE-based compact model that captures the switching dynamics of such devices. In this work, we investigate a low-power, forming-free resistive memory device with graphene electrodes and a multi-layered $ TiO_{x} $/$ Al_{2} %$ O_{3} $/$ TiO_{2} $ dielectric stack. We first develop a compact model to demonstrate that the switching dynamics can be simulated by considering permanent conductive filaments in the $ TiO_{x} $ and $ TiO_{2} $ layers and by the modulation of a tunnelling gap within the $ Al_{2} %$ O_{3} $ layer. The developed devices also exhibit strong rectification behavior in the ON state. We incorporate this rectification behavior in the developed compact model. We also demonstrate that multiple filaments govern the switching dynamics at higher operating current values. The developed model also accurately captures the stochastic variability experimentally observed in the ReRAM devices. 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spice compact model for forming-free, low-power graphene-insulator-graphene reram technology |
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A SPICE compact model for forming-free, low-power graphene-insulator-graphene ReRAM technology |
| abstract |
Abstract Development of scalable, low-power resistive memory devices (ReRAM) can be crucial for energy efficient neural networks with enhanced compute-in-memory capability. Recent demonstrations show promise for graphene as an electrode material for ultra-low power switching in ReRAMs. However, a limited amount of research has been carried out towards developing a SPICE-based compact model that captures the switching dynamics of such devices. In this work, we investigate a low-power, forming-free resistive memory device with graphene electrodes and a multi-layered $ TiO_{x} $/$ Al_{2} %$ O_{3} $/$ TiO_{2} $ dielectric stack. We first develop a compact model to demonstrate that the switching dynamics can be simulated by considering permanent conductive filaments in the $ TiO_{x} $ and $ TiO_{2} $ layers and by the modulation of a tunnelling gap within the $ Al_{2} %$ O_{3} $ layer. The developed devices also exhibit strong rectification behavior in the ON state. We incorporate this rectification behavior in the developed compact model. We also demonstrate that multiple filaments govern the switching dynamics at higher operating current values. The developed model also accurately captures the stochastic variability experimentally observed in the ReRAM devices. This work shows promise for simulation of large-scale networks of graphene-based low-power ReRAM technology. © Qatar University and Springer Nature Switzerland AG 2021 |
| abstractGer |
Abstract Development of scalable, low-power resistive memory devices (ReRAM) can be crucial for energy efficient neural networks with enhanced compute-in-memory capability. Recent demonstrations show promise for graphene as an electrode material for ultra-low power switching in ReRAMs. However, a limited amount of research has been carried out towards developing a SPICE-based compact model that captures the switching dynamics of such devices. In this work, we investigate a low-power, forming-free resistive memory device with graphene electrodes and a multi-layered $ TiO_{x} $/$ Al_{2} %$ O_{3} $/$ TiO_{2} $ dielectric stack. We first develop a compact model to demonstrate that the switching dynamics can be simulated by considering permanent conductive filaments in the $ TiO_{x} $ and $ TiO_{2} $ layers and by the modulation of a tunnelling gap within the $ Al_{2} %$ O_{3} $ layer. The developed devices also exhibit strong rectification behavior in the ON state. We incorporate this rectification behavior in the developed compact model. We also demonstrate that multiple filaments govern the switching dynamics at higher operating current values. The developed model also accurately captures the stochastic variability experimentally observed in the ReRAM devices. This work shows promise for simulation of large-scale networks of graphene-based low-power ReRAM technology. © Qatar University and Springer Nature Switzerland AG 2021 |
| abstract_unstemmed |
Abstract Development of scalable, low-power resistive memory devices (ReRAM) can be crucial for energy efficient neural networks with enhanced compute-in-memory capability. Recent demonstrations show promise for graphene as an electrode material for ultra-low power switching in ReRAMs. However, a limited amount of research has been carried out towards developing a SPICE-based compact model that captures the switching dynamics of such devices. In this work, we investigate a low-power, forming-free resistive memory device with graphene electrodes and a multi-layered $ TiO_{x} $/$ Al_{2} %$ O_{3} $/$ TiO_{2} $ dielectric stack. We first develop a compact model to demonstrate that the switching dynamics can be simulated by considering permanent conductive filaments in the $ TiO_{x} $ and $ TiO_{2} $ layers and by the modulation of a tunnelling gap within the $ Al_{2} %$ O_{3} $ layer. The developed devices also exhibit strong rectification behavior in the ON state. We incorporate this rectification behavior in the developed compact model. We also demonstrate that multiple filaments govern the switching dynamics at higher operating current values. The developed model also accurately captures the stochastic variability experimentally observed in the ReRAM devices. This work shows promise for simulation of large-scale networks of graphene-based low-power ReRAM technology. © Qatar University and Springer Nature Switzerland AG 2021 |
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A SPICE compact model for forming-free, low-power graphene-insulator-graphene ReRAM technology |
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https://dx.doi.org/10.1007/s42247-021-00265-8 |
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Pande, Shubham R. Roy, Tania Vogel, Eric M. Chakravorty, Anjan Chakrabarti, Bhaswar |
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Pande, Shubham R. Roy, Tania Vogel, Eric M. Chakravorty, Anjan Chakrabarti, Bhaswar |
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10.1007/s42247-021-00265-8 |
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2024-07-04T02:29:59.739Z |
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| score |
7.3999014 |