Issue

Vol. 16 (2024)

Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials

https://doi.org/10.1007/s40820-024-01335-2
Hangbo Zhou
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16‑16 Connexis, Singapore 138632, Republic of Singapore
Sifan Li
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Republic of Singapore
Kah‑Wee Ang
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Republic of Singapore
Yong‑Wei Zhang
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16‑16 Connexis, Singapore 138632, Republic of Singapore

Corresponding Author: Yong‑Wei Zhang

zhangyw@ihpc.a-star.edu.sg

Nano-Micro Letters, Vol. 16 (2024), Article Number: 121

Abstract

The conventional computing architecture faces substantial challenges, including high latency and energy consumption between memory and processing units. In response, in-memory computing has emerged as a promising alternative architecture, enabling computing operations within memory arrays to overcome these limitations. Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays, rapid response times, and ability to emulate biological synapses. Among these devices, two-dimensional (2D) material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing, thanks to their exceptional performance driven by the unique properties of 2D materials, such as layered structures, mechanical flexibility, and the capability to form heterojunctions. This review delves into the state-of-the-art research on 2D material-based memristive arrays, encompassing critical aspects such as material selection, device performance metrics, array structures, and potential applications. Furthermore, it provides a comprehensive overview of the current challenges and limitations associated with these arrays, along with potential solutions. The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing, leveraging the potential of 2D material-based memristive devices.


Highlights:
1 State-of-the-art research on two-dimensional material-based memristive arrays is comprehensively reviewed.
2 Critical steps in achieving in-memory computing are identified and highlighted, covering material selection, device performance analysis, and array structure design.
3 Challenges in progressing from single-device characterization to array-level and system-level implementations are discussed, along with proposed solutions.

Keywords

2D materials Memristors Memtransistors Crossbar array In-memory computing
Zhou, Hangbo, Sifan Li, Kah‑Wee Ang, and Yong‑Wei Zhang. 2024. “Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays With 2D Materials”. Nano-Micro Letters 16 (February):121. https://doi.org/10.1007/s40820-024-01335-2.
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. W. Zhang, B. Gao, J. Tang, P. Yao, S. Yu et al., Neuro-inspired computing chips. Nat. Electron. 3, 371–382 (2020). https://doi.org/10.1038/s41928-020-0435-7
  2. D. Kuzum, S. Yu, H.-S. Philip Wong, Synaptic electronics: materials, devices and applications. Nanotechnology 24, 382001 (2013). https://doi.org/10.1088/0957-4484/24/38/382001
  3. D. Li, X. Liang, Neurons mimicked by electronics. Nature 554, 472–473 (2018). https://doi.org/10.1038/d41586-018-02025-x
  4. G. Indiveri, B. Linares-Barranco, R. Legenstein, G. Deligeorgis, T. Prodromakis, Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology 24, 384010 (2013). https://doi.org/10.1088/0957-4484/24/38/384010
  5. V.K. Sangwan, M.C. Hersam, Neuromorphic nanoelectronic materials. Nat. Nanotechnol. 15, 517–528 (2020). https://doi.org/10.1038/s41565-020-0647-z
  6. Y.-C. Chen, C.-Y. Lin, H. Cho, S. Kim, B. Fowler et al., Current-sweep operation on nonlinear selectorless RRAM for multilevel cell applications. J. Electron. Mater. 49, 3499–3503 (2020). https://doi.org/10.1007/s11664-020-07987-1
  7. Q. Xia, J.J. Yang, Memristive crossbar arrays for brain-inspired computing. Nat. Mater. 18, 309–323 (2019). https://doi.org/10.1038/s41563-019-0291-x
  8. S. Yu, Neuro-inspired computing with emerging nonvolatile memorys. Proc. IEEE 106, 260 (2018). https://doi.org/10.1109/JPROC.2018.2790840
  9. A. Sebastian, M. Le Gallo, R. Khaddam-Aljameh, E. Eleftheriou, Memory devices and applications for in-memory computing. Nat. Nanotechnol. 15, 529–544 (2020). https://doi.org/10.1038/s41565-020-0655-z
  10. E.J. Fuller, S.T. Keene, A. Melianas, Z. Wang, S. Agarwal et al., Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 364, 570–574 (2019). https://doi.org/10.1126/science.aaw5581
  11. J.J. Yang, D.B. Strukov, D.R. Stewart, Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013). https://doi.org/10.1038/nnano.2012.240
  12. M. Prezioso, M.R. Mahmoodi, F.M. Bayat, H. Nili, H. Kim et al., Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits. Nat. Commun. 9, 5311 (2018). https://doi.org/10.1038/s41467-018-07757-y
  13. C. Li, M. Hu, Y. Li, H. Jiang, N. Ge et al., Analogue signal and image processing with large memristor crossbars. Nat. Electron. 1, 52–59 (2018). https://doi.org/10.1038/s41928-017-0002-z
  14. M. Hu, C.E. Graves, C. Li, Y. Li, N. Ge et al., Memristor-based analog computation and neural network classification with a dot product engine. Adv. Mater. 30, 1705914 (2018). https://doi.org/10.1002/adma.201705914
  15. Y. Li, K.-W. Ang, Hardware implementation of neuromorphic computing using large-scale memristor crossbar arrays. Adv. Intell. Syst. 3, 2000137 (2021). https://doi.org/10.1002/aisy.202000137
  16. M.A. Lastras-Montaño, K.-T. Cheng, Resistive random-access memory based on ratioed memristors. Nat. Electron. 1, 466–472 (2018). https://doi.org/10.1038/s41928-018-0115-z
  17. A. Thomas, Memristor-based neural networks. J. Phys D-Appl. Phys. 46, 093001 (2013). https://doi.org/10.1088/0022-3727/46/9/093001
  18. L. Chua, Resistance switching memories are memristors. Appl. Phys. A 102, 765–783 (2011). https://doi.org/10.1007/s00339-011-6264-9
  19. Y. Xi, B. Gao, J. Tang, A. Chen, M.-F. Chang et al., In-memory learning with analog resistive switching memory: a review and perspective. Proc. IEEE 109, 14–42 (2021). https://doi.org/10.1109/JPROC.2020.3004543
  20. D.B. Strukov, G.S. Snider, D.R. Stewart, R.S. Williams, The missing memristor found. Nature 453, 80–83 (2008). https://doi.org/10.1038/nature06932
  21. L. Chua, Memristor-the missing circuit element. IEEE Trans. Circuit Theory 18, 507–519 (1971). https://doi.org/10.1109/TCT.1971.1083337
  22. H.-S.P. Wong, H.-Y. Lee, S. Yu, Y.-S. Chen, Y. Wu et al., Metal–oxide RRAM. Proc. IEEE 100, 1951–1970 (2012). https://doi.org/10.1109/JPROC.2012.2190369
  23. R. Waser, R. Dittmann, G. Staikov, K. Szot, Redox-based resistive switching memories: nanoionic mechanisms, prospects, and challenges. Adv. Mater. 21, 2632–2663 (2009). https://doi.org/10.1002/adma.200900375
  24. G.W. Burr, R.M. Shelby, S. Sidler, C. di Nolfo, J. Jang et al., Experimental demonstration and tolerancing of a large-scale neural network (165 000 synapses) using phase-change memory as the synaptic weight element. IEEE Trans. Electron Dev. 62, 3498–3507 (2015). https://doi.org/10.1109/TED.2015.2439635
  25. V. Joshi, M. Le Gallo, S. Haefeli, I. Boybat, S.R. Nandakumar et al., Accurate deep neural network inference using computational phase-change memory. Nat. Commun. 11, 2473 (2020). https://doi.org/10.1038/s41467-020-16108-9
  26. S. Choi, S.H. Tan, Z. Li, Y. Kim, C. Choi et al., SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations. Nat. Mater. 17, 335–340 (2018). https://doi.org/10.1038/s41563-017-0001-5
  27. J. Lee, C. Du, K. Sun, E. Kioupakis, W.D. Lu, Tuning ionic transport in memristive devices by graphene with engineered nanopores. ACS Nano 10, 3571–3579 (2016). https://doi.org/10.1021/acsnano.5b07943
  28. J.H. Yoon, J.H. Han, J.S. Jung, W. Jeon, G.H. Kim et al., Highly improved uniformity in the resistive switching parameters of TiO2 thin films by inserting Ru nanodots. Adv. Mater. 25, 1987–1992 (2013). https://doi.org/10.1002/adma.201204572
  29. W.-Y. Chang, C.-A. Lin, J.-H. He, T.-B. Wu, Resistive switching behaviors of ZnO nanorod layers. Appl. Phys. Lett. 96, 242109 (2010). https://doi.org/10.1063/1.3453450
  30. H.-P. Wong, S. Raoux, S. Kim, J. Liang, J.P. Reifenberg et al., Phase change memory. Proc. IEEE 98, 2201 (2010). https://doi.org/10.1109/JPROC.2010.2070050
  31. C. Liu, H. Chen, S. Wang, Q. Liu, Y.G. Jiang et al., Two-dimensional materials for next-generation computing technologies. Nat. Nanotechnol. 15, 545–557 (2020). https://doi.org/10.1038/s41565-020-0724-3
  32. F. Zhang, H. Zhang, S. Krylyuk, C.A. Milligan, Y. Zhu et al., Electric-field induced structural transition in vertical MoTe2- and Mo1-xWxTe2-based resistive memories. Nat. Mater. 18, 55–61 (2019). https://doi.org/10.1038/s41563-018-0234-y
  33. A.A. Bessonov, M.N. Kirikova, D.I. Petukhov, M. Allen, T. Ryhänen et al., Layered memristive and memcapacitive switches for printable electronics. Nat. Mater. 14, 199–204 (2015). https://doi.org/10.1038/nmat4135
  34. X. Feng, Y. Li, L. Wang, S. Chen, Z.G. Yu et al., Neuromorphic computing: a fully printed flexible MoS2 memristive artificial synapse with femtojoule switching energy. Adv. Electron. Mater. 5, 1970061 (2019). https://doi.org/10.1002/aelm.201970061
  35. P. Cheng, K. Sun, Y.H. Hu, Memristive behavior and ideal memristor of 1T phase MoS2 nanosheets. Nano Lett. 16, 572–576 (2016). https://doi.org/10.1021/acs.nanolett.5b04260
  36. W. Huh, S. Jang, J.Y. Lee, D. Lee, D. Lee et al., Synaptic barristor based on phase-engineered 2D heterostructures. Adv. Mater. 30, e1801447 (2018). https://doi.org/10.1002/adma.201801447
  37. C. Zhang, H. Zhou, S. Chen, G. Zhang, Z.G. Yu et al., Recent progress on 2D materials-based artificial synapses. Crit. Rev. Solid State Mater. Sci. 47, 665–690 (2022). https://doi.org/10.1080/10408436.2021.1935212
  38. Y. Li, S. Chen, Z. Yu, S. Li, Y. Xiong et al., In-memory computing using memristor arrays with ultrathin 2D PdSeOx/PdSe2 heterostructure. Adv. Mater. 34, e2201488 (2022). https://doi.org/10.1002/adma.202201488
  39. H. Zhao, Z. Dong, H. Tian, D. DiMarzi, M.-G. Han et al., Atomically thin femtojoule memristive device. Adv. Mater. 29, 1703232 (2017). https://doi.org/10.1002/adma.201703232
  40. R. Ge, X. Wu, M. Kim, J. Shi, S. Sonde et al., Atomristor: nonvolatile resistance switching in atomic sheets of transition metal dichalcogenides. Nano Lett. 18, 434–441 (2018). https://doi.org/10.1021/acs.nanolett.7b04342
  41. R. Xu, H. Jang, M.-H. Lee, D. Amanov, Y. Cho et al., Vertical MoS2 double-layer memristor with electrochemical metallization as an atomic-scale synapse with switching thresholds approaching 100 mV. Nano Lett. 19, 2411–2417 (2019). https://doi.org/10.1021/acs.nanolett.8b05140
  42. X. Wu, R. Ge, P.-A. Chen, H. Chou, Z. Zhang et al., Thinnest nonvolatile memory based on monolayer h-BN. Adv. Mater. 31, e1806790 (2019). https://doi.org/10.1002/adma.201806790
  43. S. Wang, C.-Y. Wang, P. Wang, C. Wang, Z.-A. Li et al., Networking retinomorphic sensor with memristive crossbar for brain-inspired visual perception. Natl. Sci. Rev. 8, nwaa172 (2020). https://doi.org/10.1093/nsr/nwaa172
  44. V.K. Sangwan, H.-S. Lee, H. Bergeron, I. Balla, M.E. Beck et al., Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide. Nature 554, 500–504 (2018). https://doi.org/10.1038/nature25747
  45. Y.S. Ang, L. Cao, L.K. Ang, Physics of electron emission and injection in two-dimensional materials: theory and simulation. InfoMat 3, 502–535 (2021). https://doi.org/10.1002/inf2.12168
  46. D. Akinwande, C. Huyghebaert, C.H. Wang, M.I. Serna, S. Goossens et al., Graphene and two-dimensional materials for silicon technology. Nature 573, 507–518 (2019). https://doi.org/10.1038/s41586-019-1573-9
  47. L. Sun, Y. Zhang, G. Han, G. Hwang, J. Jiang et al., Self-selective van der Waals heterostructures for large scale memory array. Nat. Commun. 10, 3161 (2019). https://doi.org/10.1038/s41467-019-11187-9
  48. M. Wang, S. Cai, C. Pan, C. Wang, X. Lian et al., Robust memristors based on layered two-dimensional materials. Nat. Electron. 1, 130–136 (2018). https://doi.org/10.1038/s41928-018-0021-4
  49. C.-Y. Wang, S.-J. Liang, S. Wang, P. Wang, Z.-A. Li et al., Gate-tunable van der Waals heterostructure for reconfigurable neural network vision sensor. Sci. Adv. 6, eaba6173 (2020). https://doi.org/10.1126/sciadv.aba6173
  50. S. Seo, S.H. Jo, S. Kim, J. Shim, S. Oh et al., Artificial optic-neural synapse for colored and color-mixed pattern recognition. Nat. Commun. 9, 5106 (2018). https://doi.org/10.1038/s41467-018-07572-5
  51. K. Zhu, X. Liang, B. Yuan, M.A. Villena, C. Wen et al., Graphene-boron nitride-graphene cross-point memristors with three stable resistive states. ACS Appl. Mater. Interfaces 11, 37999–38005 (2019). https://doi.org/10.1021/acsami.9b04412
  52. C. Choi, J. Leem, M. Kim, A. Taqieddin, C. Cho et al., Curved neuromorphic image sensor array using a MoS2-organic heterostructure inspired by the human visual recognition system. Nat. Commun. 11, 5934 (2020). https://doi.org/10.1038/s41467-020-19806-6
  53. L. Mennel, J. Symonowicz, S. Wachter, D.K. Polyushkin, A.J. Molina-Mendoza et al., Ultrafast machine vision with 2D material neural network image sensors. Nature 579, 62–66 (2020). https://doi.org/10.1038/s41586-020-2038-x
  54. L. Chen, Z.G. Yu, D. Liang, S. Li, W.C. Tan et al., Ultrasensitive and robust two-dimensional indium selenide flexible electronics and sensors for human motion detection. Nano Energy 76, 105020 (2020). https://doi.org/10.1016/j.nanoen.2020.105020
  55. S. Chen, M.R. Mahmoodi, Y. Shi, C. Mahata, B. Yuan et al., Wafer-scale integration of two-dimensional materials in high-density memristive crossbar arrays for artificial neural networks. Nat. Electron. 3, 638–645 (2020). https://doi.org/10.1038/s41928-020-00473-w
  56. M. Sivan, Y. Li, H. Veluri, Y. Zhao, B. Tang et al., All WSe2 1T1R resistive RAM cell for future monolithic 3D embedded memory integration. Nat. Commun. 10, 5201 (2019). https://doi.org/10.1038/s41467-019-13176-4
  57. C.-Y. Wang, C. Wang, F. Meng, P. Wang, S. Wang et al., 2D layered materials for memristive and neuromorphic applications. Adv. Electron. Mater. 6, 1901107 (2020). https://doi.org/10.1002/aelm.201901107
  58. G. Cao, P. Meng, J. Chen, H. Liu, R. Bian et al., 2D material based synaptic devices for neuromorphic computing. Adv. Funct. Mater. 31, 2005443 (2021). https://doi.org/10.1002/adfm.202005443
  59. Z. Zhang, D. Yang, H. Li, C. Li, Z. Wang et al., 2D materials and van der Waals heterojunctions for neuromorphic computing. Neuromorph. Comput. Eng. 2, 032004 (2022). https://doi.org/10.1088/2634-4386/ac8a6a
  60. G. Lee, J.-H. Baek, F. Ren, S.J. Pearton, G.-H. Lee et al., Artificial neuron and synapse devices based on 2D materials. Small 17, 2100640 (2021). https://doi.org/10.1002/smll.202100640
  61. K. Liao, P. Lei, M. Tu, S. Luo, T. Jiang et al., Memristor based on inorganic and organic two-dimensional materials: mechanisms, performance, and synaptic applications. ACS Appl. Mater. Interfaces 13, 32606–32623 (2021). https://doi.org/10.1021/acsami.1c07665
  62. J. Bian, Z. Cao, P. Zhou, Neuromorphic computing: devices, hardware, and system application facilitated by two-dimensional materials. Appl. Phys. Rev. 8, 041313 (2021). https://doi.org/10.1063/5.0067352
  63. F. Zhang, C. Li, Z. Li, L. Dong, J. Zhao, Recent progress in three-terminal artificial synapses based on 2D materials: from mechanisms to applications. Microsyst. Nanoeng. 9, 16 (2023). https://doi.org/10.1038/s41378-023-00487-2
  64. X. Liu, Z. Zeng, Memristor crossbar architectures for implementing deep neural networks. Complex Intell. Syst. 8, 787–802 (2022). https://doi.org/10.1007/s40747-021-00282-4
  65. D. Ielmini, H.-S.P. Wong, In-memory computing with resistive switching devices. Nat. Electron. 1, 333–343 (2018). https://doi.org/10.1038/s41928-018-0092-2
  66. S. Yu, H.Y. Chen, B. Gao, J. Kang, H.S. Wong, HfOx-based vertical resistive switching random access memory suitable for bit-cost-effective three-dimensional cross-point architecture. ACS Nano 7, 2320–2325 (2013). https://doi.org/10.1021/nn305510u
  67. C.-H. Yeh, D. Zhang, W. Cao, K. Banerjee, 0.5T0.5R - introducing an ultra-compact memory cell enabled by shared graphene edge-contact and h-BN insulator, in 2020 IEEE International Electron Devices Meeting (IEDM). San Francisco, CA, USA. IEEE, (2020)., 12.3.1–12.3.4
  68. H.-S. Lee, V.K. Sangwan, W.A.G. Rojas, H. Bergeron, H.Y. Jeong et al., Dual-gated MoS2 memtransistor crossbar array. Adv. Funct. Mater. 30, 2003683 (2020). https://doi.org/10.1002/adfm.202003683
  69. J. Xie, S. Afshari, I. SanchezEsqueda, Hexagonal boron nitride (h-BN) memristor arrays for analog-based machine learning hardware npj 2D Mater. Appl. 6, 50 (2022). https://doi.org/10.1038/s41699-022-00328-2
  70. M. Naqi, M.S. Kang, N. liu, T. Kim, S. Baek, et al., Multilevel artificial electronic synaptic device of direct grown robust MoS2 based memristor array for in-memory deep neural network npj 2D Mater. Appl. 6, 53 (2022). https://doi.org/10.1038/s41699-022-00325-5
  71. S. Li, M.-E. Pam, Y. Li, L. Chen, Y.-C. Chien et al., Wafer-scale 2D hafnium diselenide based memristor crossbar array for energy-efficient neural network hardware. Adv. Mater. 34, e2103376 (2022). https://doi.org/10.1002/adma.202103376
  72. Y. Shi, X. Liang, B. Yuan, V. Chen, H. Li et al., Electronic synapses made of layered two-dimensional materials. Nat. Electron. 1, 458–465 (2018). https://doi.org/10.1038/s41928-018-0118-9
  73. Y. Li, L. Loh, S. Li, L. Chen, B. Li et al., Anomalous resistive switching in memristors based on two-dimensional palladium diselenide using heterophase grain boundaries. Nat. Electron. 4, 348–356 (2021). https://doi.org/10.1038/s41928-021-00573-1
  74. M.A. Villena, F. Hui, X. Liang, Y. Shi, B. Yuan et al., Variability of metal/h-BN/metal memristors grown via chemical vapor deposition on different materials. Microelectron. Reliab. 102, 113410 (2019). https://doi.org/10.1016/j.microrel.2019.113410
  75. J.B. Roldan, D. Maldonado, C. Aguilera-Pedregosa, F.J. Alonso, Y. Xiao et al., Modeling the variability of Au/Ti/h-BN/Au memristive devices. IEEE Trans. Electron Devices 70, 1533–1539 (2023). https://doi.org/10.1109/TED.2022.3197677
  76. M.E. Pam, S. Li, T. Su, Y.C. Chien, Y. Li et al., Interface-modulated resistive switching in Mo-irradiated ReS2 for neuromorphic computing. Adv. Mater. 34, e2202722 (2022). https://doi.org/10.1002/adma.202202722
  77. L. Wang, W. Liao, S.L. Wong, Z.G. Yu, S. Li et al., Artificial synapses based on multiterminal memtransistors for neuromorphic application. Adv. Funct. Mater. 29, 1901106 (2019). https://doi.org/10.1002/adfm.201901106
  78. S. Li, B. Li, X. Feng, L. Chen, Y. Li et al., Electron-beam-irradiated rhenium disulfide memristors with low variability for neuromorphic computing npj 2D Mater. Appl. 5, 1 (2021). https://doi.org/10.1038/s41699-020-00190-0
  79. J. Jadwiszczak, D. Keane, P. Maguire, C.P. Cullen, Y. Zhou et al., MoS2 memtransistors fabricated by localized helium ion beam irradiation. ACS Nano 13, 14262–14273 (2019). https://doi.org/10.1021/acsnano.9b07421
  80. D. Li, B. Wu, X. Zhu, J. Wang, B. Ryu et al., MoS2 memristors exhibiting variable switching characteristics toward biorealistic synaptic emulation. ACS Nano 12, 9240–9252 (2018). https://doi.org/10.1021/acsnano.8b03977
  81. L. Sun, Z. Wang, J. Jiang, Y. Kim, B. Joo et al., In-sensor reservoir computing for language learning via two-dimensional memristors. Sci. Adv. 7, 1455 (2021). https://doi.org/10.1126/sciadv.abg1455
  82. G. Moon, S.Y. Min, C. Han, S.H. Lee, H. Ahn et al., Atomically thin synapse networks on van der Waals photo-memtransistors. Adv. Mater. 35, e2203481 (2023). https://doi.org/10.1002/adma.202203481
  83. X. Zhu, D. Li, X. Liang, W.D. Lu, Ionic modulation and ionic coupling effects in MoS2 devices for neuromorphic computing. Nat. Mater. 18, 141–148 (2019). https://doi.org/10.1038/s41563-018-0248-5
  84. N.T. Duong, Y.-C. Chien, H. Xiang, S. Li, H. Zheng et al., Dynamic ferroelectric transistor-based reservoir computing for spatiotemporal information processing. Adv. Intell. Syst. 5, 2300009 (2023). https://doi.org/10.1002/aisy.202300009
  85. K. Liu, B. Dang, T. Zhang, Z. Yang, L. Bao et al., Multilayer reservoir computing based on ferroelectric α-In2 Se3 for hierarchical information processing. Adv. Mater. 34, e2108826 (2022). https://doi.org/10.1002/adma.202108826
  86. X. Feng, S. Li, S.L. Wong, S. Tong, L. Chen et al., Self-selective multi-terminal memtransistor crossbar array for In-memory computing. ACS Nano 15, 1764–1774 (2021). https://doi.org/10.1021/acsnano.0c09441
  87. J.-J. Huang, Y.-M. Tseng, W.-C. Luo, C.-W. Hsu, T.-H. Hou, One selector-one resistor (1S1R) crossbar array for high-density flexible memory applications, in 2011 International Electron Devices Meeting. Washington, DC, USA. IEEE, (2011)., 31.7.1–31.7.4
  88. K. Zhang, Y. Feng, F. Wang, Z. Yang, J. Wang, Two dimensional hexagonal boron nitride (2D-hBN): synthesis, properties and applications. J. Mater. Chem. C 5, 11992–12022 (2017). https://doi.org/10.1039/C7TC04300G
  89. K. Zhu, S. Pazos, F. Aguirre, Y. Shen, Y. Yuan et al., Hybrid 2D-CMOS microchips for memristive applications. Nature 618, 57–62 (2023). https://doi.org/10.1038/s41586-023-05973-1
  90. B. Tang, H. Veluri, Y. Li, Z.G. Yu, M. Waqar et al., Wafer-scale solution-processed 2D material analog resistive memory array for memory-based computing. Nat. Commun. 13, 3037 (2022). https://doi.org/10.1038/s41467-022-30519-w
  91. R. Yue, A.T. Barton, H. Zhu, A. Azcatl, L.F. Pena et al., HfSe2 thin films: 2D transition metal dichalcogenides grown by molecular beam epitaxy. ACS Nano 9, 474–480 (2015). https://doi.org/10.1021/nn5056496
  92. M.J. Mleczko, C. Zhang, H.R. Lee, H.H. Kuo, B. Magyari-Köpe et al., HfSe2 and ZrSe2: two-dimensional semiconductors with native high-κ oxides. Sci. Adv. 3, e1700481 (2017). https://doi.org/10.1126/sciadv.1700481
  93. V.G. Pleshchev, N.V. Selezneva, N.V. Baranov, Influence of copper intercalation on the resistive state of compounds in the Cu-HfSe2 system. Phys. Solid State 54, 716–721 (2012). https://doi.org/10.1134/S1063783412040221
  94. V.G. Pleshchev, N.V. Melnikova, N.V. Baranov, Relaxation processes in an alternating-current electric field and energy loss mechanisms in hafnium diselenide cointercalated with copper and silver atoms. Phys. Solid State 58, 1758–1763 (2016). https://doi.org/10.1134/S1063783416090274
  95. L. Liu, Y. Li, X. Huang, J. Chen, Z. Yang et al., Low-power memristive logic device enabled by controllable oxidation of 2D HfSe2 for In-memory computing. Adv. Sci. 8, e2005038 (2021). https://doi.org/10.1002/advs.202005038
  96. Y. Wang, F. Wu, X. Liu, J. Lin, J.-Y. Chen et al., High on/off ratio black phosphorus based memristor with ultra-thin phosphorus oxide layer. Appl. Phys. Lett. 115, 193503 (2019). https://doi.org/10.1063/1.5115531
  97. H. Zhou, V. Sorkin, S. Chen, Z. Yu, K.-W. Ang et al., Design-dependent switching mechanisms of schottky-barrier-modulated memristors based on 2D semiconductor. Adv. Electron. Mater. 9, 2201252 (2023). https://doi.org/10.1002/aelm.202201252
  98. Q. Fang, X. Zhao, C. Xia, F. Ma, Interfacial defect engineering on electronic states and electrical properties of MoS2/metal contacts. J. Alloys Compd. 864, 158134 (2021). https://doi.org/10.1016/j.jallcom.2020.158134
  99. W.S. Yun, J.D. Lee, Schottky barrier tuning of the single-layer MoS2 on magnetic metal substrates through vacancy defects and hydrogenation. Phys. Chem. Chem. Phys. 18, 31027–31032 (2016). https://doi.org/10.1039/C6CP05384J
  100. J. Yuan, S.E. Liu, A. Shylendra, W.A. Gaviria Rojas, S. Guo et al., Reconfigurable MoS2 memtransistors for continuous learning in spiking neural networks. Nano Lett. 21, 6432–6440 (2021). https://doi.org/10.1021/acs.nanolett.1c00982
  101. L. Tong, Z. Peng, R. Lin, Z. Li, Y. Wang et al., 2D materials-based homogeneous transistor-memory architecture for neuromorphic hardware. Science 373, 1353–1358 (2021). https://doi.org/10.1126/science.abg3161
  102. A. Sebastian, R. Pendurthi, A. Kozhakhmetov, N. Trainor, J.A. Robinson et al., Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks. Nat. Commun. 13, 6139 (2022). https://doi.org/10.1038/s41467-022-33699-7
  103. S. Hao, X. Ji, S. Zhong, K.Y. Pang, K.G. Lim et al., A monolayer leaky integrate-and-fire neuron for 2D memristive neuromorphic networks. Adv. Electron. Mater. 6, 1901335 (2020). https://doi.org/10.1002/aelm.201901335
  104. K. Liu, T. Zhang, B. Dang, L. Bao, L. Xu et al., An optoelectronic synapse based on α-In2Se3 with controllable temporal dynamics for multimode and multiscale reservoir computing. Nat. Electron. 5, 761–773 (2022). https://doi.org/10.1038/s41928-022-00847-2
  105. F. Miao, J. JoshuaYang, I. Valov, Y. Chai, Editorial: focus issue on 2D materials for neuromorphic computing. Neuromorph. Comput. Eng. 3, 010201 (2023). https://doi.org/10.1088/2634-4386/acba3f
  106. R. Hasan, T.M. Taha, C. Yakopcic, On-chip training of memristor crossbar based multi-layer neural networks. Microelectron. J. 66, 31–40 (2017). https://doi.org/10.1016/j.mejo.2017.05.005
  107. Y. Shen, W. Zheng, K. Zhu, Y. Xiao, C. Wen et al., Variability and yield in h-BN-based memristive circuits: the role of each type of defect. Adv. Mater. 33, e2103656 (2021). https://doi.org/10.1002/adma.202103656
  108. The International Roadmap For Devices and Systems: 2022, https://irds.ieee.org/images/files/pdf/2022/2022IRDS_BC.pdf. Accessed 8 Nov 22
  109. B. Yuan, X. Liang, L. Zhong, Y. Shi, F. Palumbo et al., 150nm × 200nm cross-point hexagonal boron nitride-based memristors. Adv. Electron. Mater. 6, 1900115 (2020). https://doi.org/10.1002/aelm.201900115
  110. Y. Zheng, H. Ravichandran, T.F. Schranghamer, N. Trainor, J.M. Redwing et al., Hardware implementation of Bayesian network based on two-dimensional memtransistors. Nat. Commun. 13, 5578 (2022). https://doi.org/10.1038/s41467-022-33053-x
  111. A. Krishnaprasad, D. Dev, S.S. Han, Y. Shen, H.S. Chung et al., MoS2 synapses with ultra-low variability and their implementation in Boolean logic. ACS Nano 16, 2866–2876 (2022). https://doi.org/10.1021/acsnano.1c09904
  112. J.B. Roldan, D. Maldonado, C. Aguilera-Pedregosa, E. Moreno, F. Aguirre et al. Spiking neural networks based on two-dimensional materials npj 2D Mater. Appl. 6, 63 (2022). https://doi.org/10.1038/s41699-022-00341-5
  113. K. Wang, L. Li, R. Zhao, J. Zhao, Z. Zhou et al., A pure 2H-MoS2 nanosheet-based memristor with low power consumption and linear multilevel storage for artificial synapse emulator. Adv. Electron. Mater. 6, 1901342 (2020). https://doi.org/10.1002/aelm.201901342
  114. M. Lanza, G. Molas, I. Naveh, The gap between academia and industry in resistive switching research. Nat. Electron. 6, 260–263 (2023). https://doi.org/10.1038/s41928-023-00954-8
  115. Y.-C. Chien, H. Xiang, J. Wang, Y. Shi, X. Fong et al., Attack resilient true random number generators using ferroelectric-enhanced stochasticity in 2D transistor. Small 19, e2302842 (2023). https://doi.org/10.1002/smll.202302842
  116. X. Chen, T. Wang, J. Shi, W. Lv, Y. Han et al., A novel artificial neuron-like gas sensor constructed from CuS quantum dots/Bi2S3 nanosheets. Nano-Micro Lett. 14, 8 (2021). https://doi.org/10.1007/s40820-021-00740-1
  117. S.A. Van, Building blocks for electronic spiking neural networks. Neural Netw. 14, 617–628 (2001). https://doi.org/10.1016/s0893-6080(01)00067-3
  118. D. Dev, A. Krishnaprasad, M.S. Shawkat, Z. He, S. Das et al., 2D MoS2-based threshold switching memristor for artificial neuron. IEEE Electron Device Lett. 41, 936–939 (2020). https://doi.org/10.1109/LED.2020.2988247
  119. Z. Zhang, S. Gao, Z. Li, Y. Xu, R. Yang et al., Artificial LIF neuron with bursting behavior based on threshold switching device. IEEE Trans. Electron Devices 70, 1374–1379 (2023). https://doi.org/10.1109/TED.2023.3236906
  120. H. Kalita, A. Krishnaprasad, N. Choudhary, S. Das, D. Dev et al., Artificial neuron using vertical MoS2/graphene threshold switching memristors. Sci. Rep. 9, 53 (2019). https://doi.org/10.1038/s41598-018-35828-z
  121. A. Dodda, N. Trainor, J.M. Redwing, S. Das, All-in-one, bio-inspired, and low-power crypto engines for near-sensor security based on two-dimensional memtransistors. Nat. Commun. 13, 3587 (2022). https://doi.org/10.1038/s41467-022-31148-z
  122. S. Fu, J.-H. Park, H. Gao, T. Zhang, X. Ji et al., Two-terminal MoS2 memristor and the homogeneous integration with a MoS2 transistor for neural networks. Nano Lett. 23, 5869–5876 (2023). https://doi.org/10.1021/acs.nanolett.2c05007
  123. P. Yao, H. Wu, B. Gao, J. Tang, Q. Zhang et al., Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–646 (2020). https://doi.org/10.1038/s41586-020-1942-4
  124. X. Wang, P. Xie, B. Chen, X. Zhang, Chip-based high-dimensional optical neural network. Nano-Micro Lett. 14, 221 (2022). https://doi.org/10.1007/s40820-022-00957-8
  125. Z. Wang, S. Joshi, S. Savel’ev, W. Song, R. Midya, et al., Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 1, 137–145 (2018). https://doi.org/10.1038/s41928-018-0023-2
  126. Y. Wang, W. Gao, S. Yang, Q. Chen, C. Ye et al., Humanoid intelligent display platform for audiovisual interaction and sound identification. Nano-Micro Lett. 15, 221 (2023). https://doi.org/10.1007/s40820-023-01199-y
  127. Y. Qiao, J. Luo, T. Cui, H. Liu, H. Tang et al., Soft electronics for health monitoring assisted by machine learning. Nano-Micro Lett. 15, 66 (2023). https://doi.org/10.1007/s40820-023-01029-1
  128. R. Wu, S. Seo, L. Ma, J. Bae, T. Kim, Full-fiber auxetic-interlaced yarn sensor for sign-language translation glove assisted by artificial neural network. Nano-Micro Lett. 14, 139 (2022). https://doi.org/10.1007/s40820-022-00887-5
  129. S.W. Cho, C. Jo, Y.H. Kim, S.K. Park, Progress of materials and devices for neuromorphic vision sensors. Nano-Micro Lett. 14, 203 (2022). https://doi.org/10.1007/s40820-022-00945-y
  130. Z. Shi, L. Meng, X. Shi, H. Li, J. Zhang et al., Morphological engineering of sensing materials for flexible pressure sensors and artificial intelligence applications. Nano-Micro Lett. 14, 141 (2022). https://doi.org/10.1007/s40820-022-00874-w
  131. J. Zeng, J. Zhao, T. Bu, G. Liu, Y. Qi et al., A flexible tribotronic artificial synapse with bioinspired neurosensory behavior. Nano-Micro Lett. 15, 18 (2022). https://doi.org/10.1007/s40820-022-00989-0
  132. K.C. Kwon, J.H. Baek, K. Hong, S.Y. Kim, H.W. Jang, Memristive devices based on two-dimensional transition metal chalcogenides for neuromorphic computing. Nano-Micro Lett. 14, 58 (2022). https://doi.org/10.1007/s40820-021-00784-3
  133. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556.
  134. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: unified, real-time object detection, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA. IEEE, (2016), 779–788. https://doi.org/10.1109/CVPR.2016.91
  135. H. Chen, T. Wan, Y. Zhou, J. Yan, C. Chen et al., Highly nonlinear memory selectors with ultrathin MoS2/WSe2/MoS2 heterojunction. Adv. Funct. Mater. (2023). https://doi.org/10.1002/adfm.202304242
  136. R. Midya, Z. Wang, J. Zhang, S.E. Savel’ev, C. Li et al., Anatomy of Ag/Hafnia-based selectors with 1010 nonlinearity. Adv. Mater. 29, 1604457 (2017). https://doi.org/10.1002/adma.201604457
Read More
Author biographies are not available.
Download this PDF file
PDF
Statistic
Read Counter : 2926 Download : 2198