Retention Secured Nonlinear and Self‐Rectifying Analog Charge Trap Memristor for Energy‐Efficient Neuromorphic Hardware
Abstract A memristive crossbar array (MCA) is an ideal platform for emerging memory and neuromorphic hardware due to its high bitwise density capability. A charge trap memristor (CTM) is an attractive candidate for the memristor cell of the MCA, because the embodied rectifying characteristic frees i...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202205654 |
| _version_ | 1797943363702358016 |
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| author | Geunyoung Kim Seoil Son Hanchan Song Jae Bum Jeon Jiyun Lee Woon Hyung Cheong Shinhyun Choi Kyung Min Kim |
| author_facet | Geunyoung Kim Seoil Son Hanchan Song Jae Bum Jeon Jiyun Lee Woon Hyung Cheong Shinhyun Choi Kyung Min Kim |
| author_sort | Geunyoung Kim |
| collection | DOAJ |
| description | Abstract A memristive crossbar array (MCA) is an ideal platform for emerging memory and neuromorphic hardware due to its high bitwise density capability. A charge trap memristor (CTM) is an attractive candidate for the memristor cell of the MCA, because the embodied rectifying characteristic frees it from the sneak current issue. Although the potential of the CTM devices has been suggested, their practical viability needs to be further proved. Here, a Pt/Ta2O5/Nb2O5‐x/Al2O3‐y/Ti CTM stack exhibiting high retention and array‐level uniformity is proposed, allowing a highly reliable selector‐less MCA. It shows high self‐rectifying and nonlinear current‐voltage characteristics below 1 µA of programming current with a continuous analog switching behavior. Also, its retention is longer than 105 s at 150 °C, suggesting the device is highly stable for non‐volatile analog applications. A plausible band diagram model is proposed based on the electronic spectroscopy results and conduction mechanism analysis. The self‐rectifying and nonlinear characteristics allow reducing the on‐chip training energy consumption by 71% for the MNIST dataset training task with an optimized programming scheme. |
| first_indexed | 2024-04-10T20:22:59Z |
| format | Article |
| id | doaj.art-4c434b1da72d4b4ab5b1f237a7140d13 |
| institution | Directory Open Access Journal |
| issn | 2198-3844 |
| language | English |
| last_indexed | 2024-04-10T20:22:59Z |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj.art-4c434b1da72d4b4ab5b1f237a7140d132023-01-25T13:47:49ZengWileyAdvanced Science2198-38442023-01-01103n/an/a10.1002/advs.202205654Retention Secured Nonlinear and Self‐Rectifying Analog Charge Trap Memristor for Energy‐Efficient Neuromorphic HardwareGeunyoung Kim0Seoil Son1Hanchan Song2Jae Bum Jeon3Jiyun Lee4Woon Hyung Cheong5Shinhyun Choi6Kyung Min Kim7Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of KoreaDepartment of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of KoreaDepartment of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of KoreaDepartment of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of KoreaSemiconductor Research & Development (SRD) Samsung Electronics Hwaseong 18448 Republic of KoreaDepartment of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of KoreaThe School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of KoreaDepartment of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of KoreaAbstract A memristive crossbar array (MCA) is an ideal platform for emerging memory and neuromorphic hardware due to its high bitwise density capability. A charge trap memristor (CTM) is an attractive candidate for the memristor cell of the MCA, because the embodied rectifying characteristic frees it from the sneak current issue. Although the potential of the CTM devices has been suggested, their practical viability needs to be further proved. Here, a Pt/Ta2O5/Nb2O5‐x/Al2O3‐y/Ti CTM stack exhibiting high retention and array‐level uniformity is proposed, allowing a highly reliable selector‐less MCA. It shows high self‐rectifying and nonlinear current‐voltage characteristics below 1 µA of programming current with a continuous analog switching behavior. Also, its retention is longer than 105 s at 150 °C, suggesting the device is highly stable for non‐volatile analog applications. A plausible band diagram model is proposed based on the electronic spectroscopy results and conduction mechanism analysis. The self‐rectifying and nonlinear characteristics allow reducing the on‐chip training energy consumption by 71% for the MNIST dataset training task with an optimized programming scheme.https://doi.org/10.1002/advs.202205654analogcharge‐trapmemristorsneuromorphicself‐rectifying |
| spellingShingle | Geunyoung Kim Seoil Son Hanchan Song Jae Bum Jeon Jiyun Lee Woon Hyung Cheong Shinhyun Choi Kyung Min Kim Retention Secured Nonlinear and Self‐Rectifying Analog Charge Trap Memristor for Energy‐Efficient Neuromorphic Hardware Advanced Science analog charge‐trap memristors neuromorphic self‐rectifying |
| title | Retention Secured Nonlinear and Self‐Rectifying Analog Charge Trap Memristor for Energy‐Efficient Neuromorphic Hardware |
| title_full | Retention Secured Nonlinear and Self‐Rectifying Analog Charge Trap Memristor for Energy‐Efficient Neuromorphic Hardware |
| title_fullStr | Retention Secured Nonlinear and Self‐Rectifying Analog Charge Trap Memristor for Energy‐Efficient Neuromorphic Hardware |
| title_full_unstemmed | Retention Secured Nonlinear and Self‐Rectifying Analog Charge Trap Memristor for Energy‐Efficient Neuromorphic Hardware |
| title_short | Retention Secured Nonlinear and Self‐Rectifying Analog Charge Trap Memristor for Energy‐Efficient Neuromorphic Hardware |
| title_sort | retention secured nonlinear and self rectifying analog charge trap memristor for energy efficient neuromorphic hardware |
| topic | analog charge‐trap memristors neuromorphic self‐rectifying |
| url | https://doi.org/10.1002/advs.202205654 |
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