J. Semicond. > 2022, Volume 43 > Issue 5 > 054102, doi: 10.1088/1674-4926/43/5/054102
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ARTICLES

Uniform, fast, and reliable CMOS compatible resistive switching memory

Yunxia Hao1, 2, Ying Zhang1, 2, Zuheng Wu3, , Xumeng Zhang4, Tuo Shi1, Yongzhou Wang1, Jiaxue Zhu1, 2, Rui Wang1, 2, Yan Wang1, 2 and Qi Liu1, 2, 4

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 Corresponding author: Zuheng Wu, wuzuheng@ahu.edu.cn

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Abstract: Resistive switching random access memory (RRAM) is considered as one of the potential candidates for next-generation memory. However, obtaining an RRAM device with comprehensively excellent performance, such as high retention and endurance, low variations, as well as CMOS compatibility, etc., is still an open question. In this work, we introduce an insert TaOx layer into HfOx-based RRAM to optimize the device performance. Attributing to robust filament formed in the TaOx layer by a forming operation, the local-field and thermal enhanced effect and interface modulation has been implemented simultaneously. Consequently, the RRAM device features large windows (> 103), fast switching speed (~ 10 ns), steady retention (> 72 h), high endurance (> 108 cycles), and excellent uniformity of both cycle-to-cycle and device-to-device. These results indicate that inserting the TaOx layer can significantly improve HfOx-based device performance, providing a constructive approach for the practical application of RRAM.

Key words: uniformity, resistance switching, field enhance layer, thermal enhance layer and interface modulation



[1]
Shi T, Wang R, Wu Z H, et al. A review of resistive switching devices: Performance improvement, characterization, and applications. Small Struct, 2021, 2, 2170010 doi: 10.1002/sstr.202170010
[2]
Wang Z R, Wu H Q, Burr G W, et al. Resistive switching materials for information processing. Nat Rev Mater, 2020, 5, 173 doi: 10.1038/s41578-019-0159-3
[3]
Li X K, Zhang B T, Wang B W, et al. Low power and high uniformity of HfO x-based RRAM via tip-enhanced electric fields. Sci China Inf Sci, 2019, 62, 1 doi: 10.1007/s11432-019-9910-x
[4]
Zhao X L, Zhang X M, Shang D S, et al. Uniform, fast, and reliable LixSiOy-based resistive switching memory. IEEE Electron Device Lett, 2019, 40, 554 doi: 10.1109/LED.2019.2900261
[5]
Chen F T, Lee H Y, Chen Y S, et al. Resistance switching for RRAM applications. Sci China Inf Sci, 2011, 54, 1073 doi: 10.1007/s11432-011-4217-8
[6]
Han R Z, Huang P, Zhao Y D, et al. Efficient evaluation model including interconnect resistance effect for large scale RRAM crossbar array matrix computing. Sci China Inf Sci, 2018, 62, 1 doi: 10.1007/s11432-018-9555-8
[7]
Xia Q F, Yang J J. Memristive crossbar arrays for brain-inspired computing. Nat Mater, 2019, 18, 309 doi: 10.1038/s41563-019-0291-x
[8]
Wu Z, Lu J, Shi T, et al. A habituation sensory nervous system with memristors. Adv Mater, 2020, 32, e2004398 doi: 10.1002/adma.202004398
[9]
Wu Z H, Zhao X L, Yang Y, et al. Transformation of threshold volatile switching to quantum point contact originated nonvolatile switching in graphene interface controlled memory devices. Nanoscale Adv, 2019, 1, 3753 doi: 10.1039/C9NA00409B
[10]
Kim H J, Park T H, Yoon K J, et al. Fabrication of a Cu-cone-shaped cation source inserted conductive bridge random access memory and its improved switching reliability. Adv Funct Mater, 2019, 29, 1806278 doi: 10.1002/adfm.201806278
[11]
Lu Y F, Li Y, Li H Y, et al. Low-power artificial neurons based on Ag/TiN/HfAlO x/Pt threshold switching memristor for neuromorphic computing. IEEE Electron Device Lett, 2020, 41, 1245 doi: 10.1109/LED.2020.3006581
[12]
Sebastian A, le Gallo M, Khaddam-Aljameh R, et al. Memory devices and applications for in-memory computing. Nat Nanotechnol, 2020, 15, 529 doi: 10.1038/s41565-020-0655-z
[13]
Li H T, Wu T F, Mitra S, et al. Device-architecture co-design for hyperdimensional computing with 3d vertical resistive switching random access memory (3D VRRAM). 2017 International Symposium on VLSI Technology, Systems and Application, 2017, 1
[14]
Liu Q, Long S, Lv H, et al. Controllable growth of nanoscale conductive filaments in solid-electrolyte-based ReRAM by using a metal nanocrystal covered bottom electrode. ACS Nano, 2010, 4, 6162 doi: 10.1021/nn1017582
[15]
Li S S, Su Y K. Improvement of the performance in Cr-doped ZnO memory devices via control of oxygen defects. RSC Adv, 2019, 9, 2941 doi: 10.1039/C8RA10112D
[16]
Zhang Y Z, Huang P, Gao B, et al. Oxide-based filamentary RRAM for deep learning. J Phys D, 2021, 54, 083002 doi: 10.1088/1361-6463/abc5e7
[17]
Liu L, Xiong W, Liu Y X, et al. Designing high-performance storage in HfO2/BiFeO3 memristor for artificial synapse applications. Adv Electron Mater, 2020, 6, 1901012 doi: 10.1002/aelm.201901012
[18]
Park J H, Jeon D S, Kim T G. Improved uniformity in the switching characteristics of ZnO-based memristors using Ti sub-oxide layers. J Phys D, 2017, 50, 015104 doi: 10.1088/1361-6463/50/1/015104
[19]
Hao Z Q, Gao B, Xu M H, et al. Cryogenic HfO x-based resistive memory with a thermal enhancement capping layer. IEEE Electron Device Lett, 2021, 42, 1276 doi: 10.1109/LED.2021.3099725
[20]
Wu W, Wu H Q, Gao B, et al. Improving analog switching in HfO x-based resistive memory with a thermal enhanced layer. IEEE Electron Device Lett, 2017, 38, 1019 doi: 10.1109/LED.2017.2719161
[21]
Ryu H, Kim S. Pseudo-interface switching of a two-terminal TaO x/HfO2 synaptic device for neuromorphic applications. Nanomaterials, 2020, 10, 1550 doi: 10.3390/nano10081550
[22]
Yoon J H, Kwon D E, Kim Y, et al. The Current limit and self-rectification functionalities in the TiO2/HfO2 resistive switching material system. Nanoscale, 2017, 9, 11920 doi: 10.1039/C7NR02215H
[23]
Wedig A, Luebben M, Cho D Y, et al. Nanoscale cation motion in TaO x, HfO x and TiO x memristive systems. Nat Nanotech, 2016, 11, 67 doi: 10.1038/nnano.2015.221
[24]
Landon C D, Wilke R H T, Brumbach M T, et al. Erratum: “Thermal transport in tantalum oxide films for memristive applications. Appl Phys Lett, 2015, 107, 059902 doi: 10.1063/1.4928532
[25]
Zhang Y, Mao G Q, Zhao X L, et al. Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging. Nat Commun, 2021, 12, 7232 doi: 10.1038/s41467-021-27575-z
[26]
Yin J, Zeng F, Wan Q, et al. Adaptive crystallite kinetics in homogenous bilayer oxide memristor for emulating diverse synaptic plasticity. Adv Funct Mater, 2018, 28, 1706927 doi: 10.1002/adfm.201706927
[27]
Miao F, Strachan J P, Yang J J, et al. Anatomy of a nanoscale conduction channel reveals the mechanism of a high-performance memristor. Adv Mater, 2011, 23, 5633 doi: 10.1002/adma.201103379
Fig. 1.  (Color online) Structure and switching performances of the Ti/TaOx/HfOx/Pt device. (a) Schematic of the Ti/ TaOx/HfOx/Pt device with a crossbar structure. The inset shows the SEM result of the as-fabricated device, the feature size of the fabricated device is 5 × 5 μm2. (b) The cyclic I–V curves of Ti/TaOx/HfOx/Pt (red) and Ti/HfOx/Pt (gray) device, which exhibits a typical bipolar resistive switching with uniform switching property. (c) The cumulative distribution of the resistance states (HRS and LRS) and operation voltages (VSET and VRESET) of Ti/TaOx/HfOx/Pt device. The HRS and LRS of Ti/TaOx/HfOx/Pt device is 17 ± 0.155 MΩ and 5 ± 0.053 kΩ, respectively. The VSET and VRESET of the device is 2.0 ± 0.16 V and –1.25 ± 0.15 V, respectively. (d) Retention characteristics of the HRS and LRS of Ti/TaOx/HfOx/Pt device for more than 3.5 × 104 s. The insets show the measurement results after 24 and 72 h, respectively. The result shows the device with good retention property in both HRS and LRS.

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Fig. 2.  (Color online) The pulse mode switching characteristics of the Ti/TaOx/HfOx/Pt devices. (a) The device response characteristics of the initialization pulse operation. (b) The switching cycles of the device under extreme fast pulse stimulus. (c) The distribution of HRS/LRS under pulse mode switching. The statistical results show that this distribution conform to the lognormal distribution, indicating a uniform switching property of the device under pulse stimulus. (d) The Ti/TaOx/HfOx/Pt device exhibits reliable endurance for more than 108 cycles, each box in the figure representing 300 switching cycles. (e) The endurance property of the Ti/HfOx/Pt device, each box in the figure representing 100 switching cycles. (f) Comparison of I–V characteristics curves of the Ti/TaOx/HfOx/Pt device before (black line) and after (red line) the pulse measurement.

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Fig. 3.  (Color online) (a) The response property of Ti/TaOx/Pt device before and after the forming process. The initial resistance state modulated by the oxygen percentage during the spurting of the TaOx process. Schematic switching mechanisms of the Ti/TaOx/HfOx/Pt device. (b) Initial state, (c) forming process, (d) RESET process and (e) SET process.

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Fig. 4.  (Color online) The cross-sectional HRTEM image and the element component profile of the device under different operation conditions. The cross-sectional HRTEM image of (a) intial state, (b) forming, (c) RESET, (d) SET. (e) The element component profile of the pristine device. The (f) CF and (g) out of CF element component profile of the device under the forming operation. The (h) CF and (i) out of CF element component profile of the device under the RESET operation. The (j) CF and (k) out of the CF element component profile of the device under SET operation.

DownLoad: Full-Size Img PowerPoint
[1]
Shi T, Wang R, Wu Z H, et al. A review of resistive switching devices: Performance improvement, characterization, and applications. Small Struct, 2021, 2, 2170010 doi: 10.1002/sstr.202170010
[2]
Wang Z R, Wu H Q, Burr G W, et al. Resistive switching materials for information processing. Nat Rev Mater, 2020, 5, 173 doi: 10.1038/s41578-019-0159-3
[3]
Li X K, Zhang B T, Wang B W, et al. Low power and high uniformity of HfO x-based RRAM via tip-enhanced electric fields. Sci China Inf Sci, 2019, 62, 1 doi: 10.1007/s11432-019-9910-x
[4]
Zhao X L, Zhang X M, Shang D S, et al. Uniform, fast, and reliable LixSiOy-based resistive switching memory. IEEE Electron Device Lett, 2019, 40, 554 doi: 10.1109/LED.2019.2900261
[5]
Chen F T, Lee H Y, Chen Y S, et al. Resistance switching for RRAM applications. Sci China Inf Sci, 2011, 54, 1073 doi: 10.1007/s11432-011-4217-8
[6]
Han R Z, Huang P, Zhao Y D, et al. Efficient evaluation model including interconnect resistance effect for large scale RRAM crossbar array matrix computing. Sci China Inf Sci, 2018, 62, 1 doi: 10.1007/s11432-018-9555-8
[7]
Xia Q F, Yang J J. Memristive crossbar arrays for brain-inspired computing. Nat Mater, 2019, 18, 309 doi: 10.1038/s41563-019-0291-x
[8]
Wu Z, Lu J, Shi T, et al. A habituation sensory nervous system with memristors. Adv Mater, 2020, 32, e2004398 doi: 10.1002/adma.202004398
[9]
Wu Z H, Zhao X L, Yang Y, et al. Transformation of threshold volatile switching to quantum point contact originated nonvolatile switching in graphene interface controlled memory devices. Nanoscale Adv, 2019, 1, 3753 doi: 10.1039/C9NA00409B
[10]
Kim H J, Park T H, Yoon K J, et al. Fabrication of a Cu-cone-shaped cation source inserted conductive bridge random access memory and its improved switching reliability. Adv Funct Mater, 2019, 29, 1806278 doi: 10.1002/adfm.201806278
[11]
Lu Y F, Li Y, Li H Y, et al. Low-power artificial neurons based on Ag/TiN/HfAlO x/Pt threshold switching memristor for neuromorphic computing. IEEE Electron Device Lett, 2020, 41, 1245 doi: 10.1109/LED.2020.3006581
[12]
Sebastian A, le Gallo M, Khaddam-Aljameh R, et al. Memory devices and applications for in-memory computing. Nat Nanotechnol, 2020, 15, 529 doi: 10.1038/s41565-020-0655-z
[13]
Li H T, Wu T F, Mitra S, et al. Device-architecture co-design for hyperdimensional computing with 3d vertical resistive switching random access memory (3D VRRAM). 2017 International Symposium on VLSI Technology, Systems and Application, 2017, 1
[14]
Liu Q, Long S, Lv H, et al. Controllable growth of nanoscale conductive filaments in solid-electrolyte-based ReRAM by using a metal nanocrystal covered bottom electrode. ACS Nano, 2010, 4, 6162 doi: 10.1021/nn1017582
[15]
Li S S, Su Y K. Improvement of the performance in Cr-doped ZnO memory devices via control of oxygen defects. RSC Adv, 2019, 9, 2941 doi: 10.1039/C8RA10112D
[16]
Zhang Y Z, Huang P, Gao B, et al. Oxide-based filamentary RRAM for deep learning. J Phys D, 2021, 54, 083002 doi: 10.1088/1361-6463/abc5e7
[17]
Liu L, Xiong W, Liu Y X, et al. Designing high-performance storage in HfO2/BiFeO3 memristor for artificial synapse applications. Adv Electron Mater, 2020, 6, 1901012 doi: 10.1002/aelm.201901012
[18]
Park J H, Jeon D S, Kim T G. Improved uniformity in the switching characteristics of ZnO-based memristors using Ti sub-oxide layers. J Phys D, 2017, 50, 015104 doi: 10.1088/1361-6463/50/1/015104
[19]
Hao Z Q, Gao B, Xu M H, et al. Cryogenic HfO x-based resistive memory with a thermal enhancement capping layer. IEEE Electron Device Lett, 2021, 42, 1276 doi: 10.1109/LED.2021.3099725
[20]
Wu W, Wu H Q, Gao B, et al. Improving analog switching in HfO x-based resistive memory with a thermal enhanced layer. IEEE Electron Device Lett, 2017, 38, 1019 doi: 10.1109/LED.2017.2719161
[21]
Ryu H, Kim S. Pseudo-interface switching of a two-terminal TaO x/HfO2 synaptic device for neuromorphic applications. Nanomaterials, 2020, 10, 1550 doi: 10.3390/nano10081550
[22]
Yoon J H, Kwon D E, Kim Y, et al. The Current limit and self-rectification functionalities in the TiO2/HfO2 resistive switching material system. Nanoscale, 2017, 9, 11920 doi: 10.1039/C7NR02215H
[23]
Wedig A, Luebben M, Cho D Y, et al. Nanoscale cation motion in TaO x, HfO x and TiO x memristive systems. Nat Nanotech, 2016, 11, 67 doi: 10.1038/nnano.2015.221
[24]
Landon C D, Wilke R H T, Brumbach M T, et al. Erratum: “Thermal transport in tantalum oxide films for memristive applications. Appl Phys Lett, 2015, 107, 059902 doi: 10.1063/1.4928532
[25]
Zhang Y, Mao G Q, Zhao X L, et al. Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging. Nat Commun, 2021, 12, 7232 doi: 10.1038/s41467-021-27575-z
[26]
Yin J, Zeng F, Wan Q, et al. Adaptive crystallite kinetics in homogenous bilayer oxide memristor for emulating diverse synaptic plasticity. Adv Funct Mater, 2018, 28, 1706927 doi: 10.1002/adfm.201706927
[27]
Miao F, Strachan J P, Yang J J, et al. Anatomy of a nanoscale conduction channel reveals the mechanism of a high-performance memristor. Adv Mater, 2011, 23, 5633 doi: 10.1002/adma.201103379

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    Received: 30 November 2021 Revised: 30 December 2021 Online: Uncorrected proof: 15 February 2022Accepted Manuscript: 15 February 2022Published: 01 May 2022

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      Article Navigation >  Journal of Semiconductors  > 2022  >  43(5): 054102
      Yunxia Hao, Ying Zhang, Zuheng Wu, Xumeng Zhang, Tuo Shi, Yongzhou Wang, Jiaxue Zhu, Rui Wang, Yan Wang, Qi Liu. Uniform, fast, and reliable CMOS compatible resistive switching memory[J]. Journal of Semiconductors, 2022, 43(5): 054102. doi: 10.1088/1674-4926/43/5/054102 Y X Hao, Y Zhang, Z H Wu, X M Zhang, T Shi, Y Z Wang, J X Zhu, R Wang, Y Wang, Q Liu. Uniform, fast, and reliable CMOS compatible resistive switching memory[J]. J. Semicond, 2022, 43(5): 054102. doi: 10.1088/1674-4926/43/5/054102Export: BibTex EndNote
      Citation:
      Yunxia Hao, Ying Zhang, Zuheng Wu, Xumeng Zhang, Tuo Shi, Yongzhou Wang, Jiaxue Zhu, Rui Wang, Yan Wang, Qi Liu. Uniform, fast, and reliable CMOS compatible resistive switching memory[J]. Journal of Semiconductors, 2022, 43(5): 054102. doi: 10.1088/1674-4926/43/5/054102

      Y X Hao, Y Zhang, Z H Wu, X M Zhang, T Shi, Y Z Wang, J X Zhu, R Wang, Y Wang, Q Liu. Uniform, fast, and reliable CMOS compatible resistive switching memory[J]. J. Semicond, 2022, 43(5): 054102. doi: 10.1088/1674-4926/43/5/054102
      Export: BibTex EndNote
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      Uniform, fast, and reliable CMOS compatible resistive switching memory

      doi: 10.1088/1674-4926/43/5/054102
      • 1.

        Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China

      • 2.

        University of Chinese Academy of Sciences, Beijing 100049, China

      • 3.

        School of Integrated Circuits, Anhui University, Hefei 230601, China

      • 4.

        Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China

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      • Author Bio:

        Yunxia Hao is currently pursuing a master's degree in Institute of Microelectronics, Chinese Academy of Sciences, Beijing. Her research focuses on resistive switching memory

        Zuheng Wu received his PhD degree from the Institute of Microelectronics, Chinese Academy of Sciences, Beijing, in 2021. His research focuses on memristors and neuromorphic computing

      • Corresponding author: wuzuheng@ahu.edu.cn
      • Received Date: 2021-11-30
      • Accepted Date: 2022-02-10
      • Revised Date: 2021-12-30
        • Available Online: 2022-04-19

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