journal article·2023-09-09Open access

Ionotronic WS2 memtransistors for 6-bit storage and neuromorphic adaptation at high temperature

Sameer Kumar Mallik, Roshan Padhan, Mousam Charan Sahu, Gopal K. Pradhan, Prasana Kumar Sahoo, Saroj Prasad Dash, Satyaprakash Sahoo

Abstract

Abstract Inspired by massive parallelism, an increase in internet-of-things devices, robust computation, and Big-data, the upsurge research in building multi-bit mem-transistors is ever-augmenting with different materials, mechanisms, and state-of-the-art architectures. Herein, we demonstrate monolayer WS2-based functional mem-transistor devices which address nonvolatility and synaptic operations at high temperature. The ionotronic memory devices based on WS2 exhibit reverse hysteresis with memory windows larger than 25 V, and extinction ratio greater than 106. The mem-transistors show stable retention and endurance greater than 100 sweep cycles and 400 pulse cycles in addition to 6-bit (64 distinct nonvolatile storage levels) pulse-programmable memory features ranging over six orders of current magnitudes (10−12–10−6 A). The origin of the multi-bit states is attributed to the carrier dynamics under electrostatic doping fluctuations induced by mobile ions, which is illustrated by employing a fingerprint mechanism including band-bending pictures. The credibility of all the storage states is confirmed by obtaining reliable signal-to-noise ratios. We also demonstrate key neuromorphic behaviors, such as synaptic plasticity, near linear potentiation, and depression, rendering it suitable for successful implementation in high temperature neuromorphic computing. Furthermore, artificial neural network simulations based on the conductance weight update characteristics of the proposed ionotronic mem-transistors are performed to explore the potency for accurate image recognition. Our findings showcase a different class of thermally aided memories based on 2D semiconductors unlocking promising avenues for high temperature memory applications in demanding electronics and forthcoming neuromorphic computing technologies.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Transition Metal Oxide Nanomaterials · Ferroelectric and Negative Capacitance Devices

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References (53)

Show source-provided references
1. Hou, X., Chen, H., Zhang, Z., Wang, S. & Zhou, P. 2D atomic crystals: a promising solution for next-generation data storage. Adv. Electron. Mater. 5, 1800944 (2019).10.1002/aelm.201800944 ↗
2. Fiori, G. et al. Electronics based on two-dimensional materials. Nat. Nanotechnol. 9, 768–779 (2014).10.1038/nnano.2014.207 ↗
3. Wang, Q. H., Kalantar-Zadeh, K., Kis, A., Coleman, J. N. & Strano, M. S. Electronics and optoelectronics of two-dimensional transition metal dichalcogenides. Nat. Nanotechnol. 7, 699–712 (2012).10.1038/nnano.2012.193 ↗
4. Migliato Marega, G. et al. Logic-in-memory based on an atomically thin semiconductor. Nature 587, 72–77 (2020).10.1038/s41586-020-2861-0 ↗
5. Xue, F. et al. Integrated memory devices based on 2D materials. Adv. Mater. 34, 2201880 (2022).10.1002/adma.202201880 ↗
6. Sahu, M. C., Sahoo, S., Mallik, S. K., Jena, A. K. & Sahoo, S. Multifunctional 2D MoS2 optoelectronic artificial synapse with integrated arithmetic and reconfigurable logic operations for in-memory neuromorphic computing applications. Adv. Mater. Technol. 8, 2201125 (2023).10.1002/admt.202201125 ↗
7. Liu, C. et al. Two-dimensional materials for next-generation computing technologies. Nat. Nanotechnol. 15, 545–557 (2020).10.1038/s41565-020-0724-3 ↗
8. Lee, D. et al. Multibit MoS2 photoelectronic memory with ultrahigh sensitivity. Adv. Mater. 28, 9196–9202 (2016).10.1002/adma.201603571 ↗
9. Lee, J. et al. Monolayer optical memory cells based on artificial trap-mediated charge storage and release. Nat. Commun. 8, 1–8 (2017).10.1038/s41467-016-0009-6 ↗
10. Wang, Q. et al. Nonvolatile infrared memory in MoS2/PbS van der Waals heterostructures. Sci. Adv. 4, eaap7916 (2018).10.1126/sciadv.aap7916 ↗
11. Yang, Z. et al. High-performance photoinduced memory with ultrafast charge transfer based on MoS2/SWCNTs network van der Waals heterostructure. Small 15, 1804661 (2019).10.1002/smll.201804661 ↗
12. Tran, M. D. et al. Two-terminal multibit optical memory via van der Waals heterostructure. Adv. Mater. 31, 1807075 (2019).10.1002/adma.201807075 ↗
13. Huang, W. et al. Multibit optoelectronic memory in top-floating-gated van der Waals heterostructures. Adv. Funct. Mater. 29, 1902890 (2019).10.1002/adfm.201902890 ↗
14. Gao, F. et al. Low optical writing energy multibit optoelectronic memory based on SnS2/h-BN/graphene heterostructure. Small 17, 2104459 (2021).10.1002/smll.202104459 ↗
15. Liu, C. et al. Polarization-resolved broadband MoS2/black phosphorus/MoS2 optoelectronic memory with ultralong retention time and ultrahigh switching ratio. Adv. Funct. Mater. 31, 2100781 (2021).10.1002/adfm.202100781 ↗
16. Wen, J. et al. Direct charge trapping multilevel memory with graphdiyne/MoS2 van der Waals heterostructure. Adv. Sci. 8, 2101417 (2021).10.1002/advs.202101417 ↗
17. Zhang, Z. C. et al. An ultrafast nonvolatile memory with low operation voltage for high-speed and low-power applications. Adv. Funct. Mater. 31, 2102571 (2021).10.1002/adfm.202102571 ↗
18. Li, Y. et al. Low-voltage ultrafast nonvolatile memory via direct charge injection through a threshold resistive-switching layer. Nat. Commun. 13, 1–9 (2022).
19. Lai, H. et al. Photoinduced multi-bit nonvolatile memory based on a van der waals heterostructure with a 2D-perovskite floating gate. Adv. Mater. 34, 2110278 (2022).10.1002/adma.202110278 ↗
20. Zhu, K. et al. Hybrid 2D/CMOS microchips for memristive applications. Nature 618, 57–62 (2023).10.1038/s41586-023-05973-1 ↗
21. Yang, C. Sen et al. A synaptic transistor based on quasi-2D molybdenum oxide. Adv. Mater. 29, 1700906 (2017).10.1002/adma.201700906 ↗
22. Jiang, J. et al. 2D MoS2 neuromorphic devices for brain-like computational systems. Small 13, 1700933 (2017).10.1002/smll.201700933 ↗
23. Yang, C. Sen et al. All-solid-state synaptic transistor with ultralow conductance for neuromorphic computing. Adv. Funct. Mater. 28, 1804170 (2018).10.1002/adfm.201804170 ↗
24. Zhu, J. et al. Ion gated synaptic transistors based on 2D van der Waals crystals with tunable diffusive dynamics. Adv. Mater. 30, 1800195 (2018).10.1002/adma.201800195 ↗
25. Yao, B. W. et al. Non-volatile electrolyte-gated transistors based on graphdiyne/MoS2 with robust stability for low-power neuromorphic computing and logic-in-memory. Adv. Funct. Mater. 31, 2100069 (2021).10.1002/adfm.202100069 ↗
26. Park, B. et al. Robust 2D MoS2 artificial synapse device based on a lithium silicate solid electrolyte for high-precision analogue neuromorphic computing. ACS Appl. Mater. Interfaces 14, 53038–53047 (2022).10.1021/acsami.2c14080 ↗
27. Li, Y. et al. Electrolyte-gated transistors with good retention for neuromorphic computing. Appl. Phys. Lett. 120, e2003018 (2022).
28. Wang, B. et al. Monolayer MoS2 synaptic transistors for high-temperature neuromorphic applications. Nano Lett. 21, 10400–10408 (2021).10.1021/acs.nanolett.1c03684 ↗
29. Schroder, D. K. & Babcock, J. A. Negative bias temperature instability: Road to cross in deep submicron silicon semiconductor manufacturing. J. Appl. Phys. 94, 1 (2003).10.1063/1.1567461 ↗
30. Watson, J. & Castro, G. A review of high-temperature electronics technology and applications. J. Mater. Sci. Mater. Electron. 26, 9226–9235 (2015).10.1007/s10854-015-3459-4 ↗
31. Mallik, S. K. et al. Salt-assisted growth of monolayer MoS2 for high-performance hysteresis-free field-effect transistor. J. Appl. Phys. 129, 145106 (2021).10.1063/5.0043884 ↗
32. Huang, X. et al. Quantitative analysis of temperature dependence of Raman shift of monolayer WS2. Sci. Rep. 6, 1–8 (2016).10.1038/s41598-016-0001-8 ↗
33. McCreary, K. M. et al. The effect of preparation conditions on Raman and photoluminescence of monolayer WS2. Sci. Rep. 6, 1–10 (2016).10.1038/s41598-016-0001-8 ↗
34. Kaushik, V. et al. Charge transport in 2D MoS2, WS2, and MoS2-WS2 heterojunction-based field-effect transistors: role of ambipolarity. J. Phys. Chem. C 124, 23368–23379 (2020).10.1021/acs.jpcc.0c05651 ↗
35. Abraham, M. & Mohney, S. E. Annealed Ag contacts to MoS2 field-effect transistors. J. Appl. Phys. 122, 115306 (2017).10.1063/1.4991961 ↗
36. He, G. et al. Thermally assisted nonvolatile memory in monolayer MoS2 transistors. Nano Lett. 16, 6445–6451 (2016).10.1021/acs.nanolett.6b02905 ↗
37. Kaushik, N. et al. Reversible hysteresis inversion in MoS2 field effect transistors. npj 2D Mater. Appl 1, 1–9 (2017).10.1038/s41699-017-0038-y ↗
38. Mallik, S. K. et al. Thermally driven multilevel non-volatile memory with monolayer MoS2 for brain-inspired artificial learning. ACS Appl. Mater. Interfaces 15, 36527–36538 (2023).10.1021/acsami.3c06336 ↗
39. Lan, C. et al. The origin of gate bias stress instability and hysteresis in monolayer WS2 transistors. Nano Res. 13, 3278–3285 (2020).10.1007/s12274-020-3003-6 ↗
40. Xu, K. et al. Electric double-layer gating of two-dimensional field-effect transistors using a single-ion conductor. ACS Appl Mater. Interfaces 11, 35879–35887 (2019).10.1021/acsami.9b11526 ↗
41. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Adv. Mater. 32, 2003984 (2020).10.1002/adma.202003984 ↗
42. Chen, M. et al. Multibit data storage states formed in plasma-treated MoS2 transistors. ACS Nano 8, 4023–4032 (2014).10.1021/nn501181t ↗
43. Bisri, S. Z., Shimizu, S., Nakano, M. & Iwasa, Y. Endeavor of iontronics: from fundamentals to applications of ion-controlled electronics. Adv. Mater. 29, 1607054 (2017).10.1002/adma.201607054 ↗
44. Lee, S. J. et al. Programmable devices based on reversible solid-state doping of two-dimensional semiconductors with superionic silver iodide. Nat. Electron. 3, 630–637 (2020).10.1038/s41928-020-00472-x ↗
45. Li, D., Ryu, B. & Liang, X. A study on MoS2-based multilevel transistor memories for neuromorphic computing. Appl. Phys. Lett. 117, 213102 (2020).10.1063/5.0030780 ↗
46. Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32, 8026–8037 (2019).
47. Deng, L. The MNIST database of handwritten digit images for machine learning research. IEEE Signal Process. Mag. 29, 141–142 (2012).10.1109/MSP.2012.2211477 ↗
48. Hao, S. et al. Monolayer MoS2/WO3 heterostructures with sulfur anion reservoirs as electronic synapses for neuromorphic computing. ACS Appl. Nano Mater. 4, 1766–1775 (2021).10.1021/acsanm.0c03205 ↗
49. Jena, A. K. et al. Bipolar resistive switching in TiO2 artificial synapse mimicking Pavlov’s associative learning. ACS Appl. Mater. Interfaces 15, 3585 (2022).
50. Seo, S. et al. The gate injection-based field-effect synapse transistor with linear conductance update for online training. Nat. Commun. 13, 1–10 (2022).
51. Gwon, O. H. et al. Systematic design and demonstration of multi-bit generation in layered materials heterostructures floating-gate memory. Adv. Funct. Mater. 31, 2105472 (2021).10.1002/adfm.202105472 ↗
52. Jia, M. et al. Multibit tribotronic nonvolatile memory based on van der Waals heterostructures. Nano Energy 83, 105785 (2021).10.1016/j.nanoen.2021.105785 ↗
53. Wang, M. et al. Robust memristors based on layered two-dimensional materials. Nat. Electron. 1, 130–136 (2018).10.1038/s41928-018-0021-4 ↗