Synaptic devices based on silicon carbide for neuromorphic computing

Boyu Ye; Xiao Liu; Chao Wu; Wensheng Yan; Xiaodong Pi

doi:10.1088/1674-4926/24100020

J. Semicond. > 2025, Volume 46 > Issue 2 > 021403

SPECIAL ISSUE REVIEWS

Synaptic devices based on silicon carbide for neuromorphic computing

Boyu Ye¹, Xiao Liu^{1, 5,}, Chao Wu⁴, Wensheng Yan¹ and Xiaodong Pi^{2, 3,}

+ Author Affiliations

1. Institute of Carbon Neutrality and New Energy, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
2. State key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China
3. Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311200, China
4. Sorbonne Université, Faculté des Sciences, CNRS, Institut Parisien de Chimie Moléculaire (IPCM), UMR 8232, 4 Place Jussieu, 75005 Paris, France
5. State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610065, China

Author Bio:

Boyu Ye is now an undergraduate student at the School of Electronics and Information, Hangzhou Dianzi University under the supervision of Dr. Xiao Liu and prof. Liang Chu. His research focuses on synaptic devices for neuromorphic computing.

Xiao Liu got her PhD degree in 2021 at Sorbonne University in France. Then she joined the group of academician Deren Yang and Prof. Xiaodong Pi at Zhejiang University as a postdoctor. In July 2023, she joined Hangzhou Dianzi University as a lecturer. Her research interests include inorganic/organic optoelectronic synaptic devices and their neuromorphic applications.

Xiaodong Pi received his PhD degree at the University of Bath in 2004. He then carried out research at McMaster University and the University of Minnesota at Twin Cities. He joined Zhejiang University as an associate professor in 2008. He is now a professor in the State Key Laboratory of Silicon and Advanced Semiconductor Materials, the School of Materials Science and Engineering, and Zhejiang University-Hangzhou Global Scientific and Technological Innovation Center. His research mainly concerns group Ⅳ semiconductor materials and devices.

Corresponding author: Xiao Liu, liuxiao@hdu.edu.cn; Xiaodong Pi, xdpi@zju.edu.cn

DOI: 10.1088/1674-4926/24100020CSTR: 32376.14.1674-4926.24100020

Abstract: To address the increasing demand for massive data storage and processing, brain-inspired neuromorphic computing systems based on artificial synaptic devices have been actively developed in recent years. Among the various materials investigated for the fabrication of synaptic devices, silicon carbide (SiC) has emerged as a preferred choices due to its high electron mobility, superior thermal conductivity, and excellent thermal stability, which exhibits promising potential for neuromorphic applications in harsh environments. In this review, the recent progress in SiC-based synaptic devices is summarized. Firstly, an in-depth discussion is conducted regarding the categories, working mechanisms, and structural designs of these devices. Subsequently, several application scenarios for SiC-based synaptic devices are presented. Finally, a few perspectives and directions for their future development are outlined.

Key words: silicon carbide, wide bandgap semiconductors, synaptic devices, neuromorphic computing, high temperature

References

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Fig. 1. (Color online) Summary of the review. According to the working mechanisms, SiC-based synaptic devices can be categorized into two types: electrically and optically stimulated synaptic devices. Commonly used materials types include amorphous SiC thin film^[58], single-crystal SiC thin film^{[44, 57, 59]}, and SiC nano wires^{[43, 60]}. Several application scenarios for neuromorphic computing include logic functions, wireless transmission, high-temperature image learning and memory, as well as high-temperature color quantization.

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Fig. 2. (Color online) (a) Schematic of the Ag/SiC/Pt structure. The device mimic (b) STDP, and (c) PPF. (d) Diagram of switching dynamics in Ag/SiC/Pt devices^[57]. (e) Schematic of the Cu/SiC/W structure. (f) Diagram of the formation of Cu conductive filament in Cu/SiC/W devices. The device mimic (g) SRDP, (h) SVDP, and (i) SDDP^[58].

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Fig. 3. (Color online) (a) Schematic of the 4H-SiC/PVK/P3HT synaptic transistor. (b) Energy band diagram of 4H-SiC, PVK, and P3HT. The device mimic (c) PPF, (d) SDDP, (e) SNDP, (f) SRDP, and (g) learning-forgetting-relearning behavior. (h) EPSC of the device triggered by 400 optical spikes, which didn’t decay completely even 10⁴ s after the stimulus stopped^[59].

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Fig. 4. (Color online) (a) Schematic diagram of the ITO/PMMA/3C-SiC nano wire/ITO synaptic device and a typical biological synapse. (b) Electron transport of the 3C-SiC nano wire device with and without light illumination. The device mimic (c) SDDP, (d) SNDP, and (e) classical conditioning of Pavlov’s dog^[60].

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Fig. 5. (Color online) (a) Schematic of a bionic human visual system, the optoelectronic memristor array, and a single synaptic device. (b) Energy band diagram of 3C-SiC and NiO. The device mimic (c) LTP/LTD, (d) SNDP, and (e) learning-forgetting-relearning behavior^[43].

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Fig. 6. (Color online) (a) Schematic of the 4H-SiC synaptic device. (b) Working mechanism of the 4H-SiC device. The device mimic (c) PPF, (d) SNDP, and (e) SRDP at 327 °C^[44].

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Fig. 7. (Color online) (a) Schematic diagram of the information integration in the synaptic device with multi-terminal inputs. (b) A spiking logic response by dual modulatory input at 0.1 and 0.5 V read voltage for achievement of "AND" and "OR" logic, respectively. (c) Histograms of the post-synaptic current for "AND" and "OR" logic at 0.1 and 0.5 V read voltage, respectively^[60].

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Fig. 8. (Color online) Encodement of the International Morse code of (a) "hello" and (b) "world" with the 365 nm light-stimulated EPSC of 3C-SiC nano wire synaptic device. Correlation between EPSC values and the International Morse code of English letter. Each letter is linearly correlated with (c) the sum and (d) the end of EPSC amplitude peak values^[60].

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Fig. 9. (Color online) The conductance response of the synapse device stimulated by different number of light pulses after decay time of (a) 0, (b) 5, and (c) 10 s. Conductance response images were obtained in the device array after applying (d) 1, (e) 5, and (f) 10 light pulses^[43].

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Fig. 10. (Color online) (a) Schematic of the array-based self-organizing map neural network. (b) Dependence of the quantization error on the number of competitive nodes. (c) Visual results of the color quantization^[44].

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Table 1. Summary of the state-of-the-art SiC-based synaptic devices.

Parameter	Ref. [57]	Ref. [58]	Ref. [59]	Ref. [60]	Ref. [43]	Ref. [44]
Stimulation	Electrical	Electrical	Optical	Optical	Electrical/optical	Optical
Working mechanism	Ion migration	Ion migration	Capture and release of carriers by heterostructures	Capture and release of carriers by heterostructures and surface traps	Ion migration/ capture and release of carriers by heterostructures	Capture and release of carriers by defects
Substrate	Si/SiO₂	Si/SiO₂	4H-SiC	Glass	Glass	4H-SiC
Active material	4H-SiC thin film	Si₇C₃ thin film	4H-SiC/PVK/P3HT thin film	3C-SiC nanowires/PMMA	3C-SiC@NiO nanowires	4H-SiC single crystal
Preparation method of SiC	Radio frequency magnetron sputtering	Chemical vapor deposition	Chemical vapor deposition (Purchase)	Electrophoretic deposition	Purchase	Chemical vapor deposition (Purchase)
Electrode	Ag/Pt	Cu/W	Au	ITO	ITO	Al/Ti/Ni, Ti/Au
Responsive frequency to electrical/light signal (Hz)	>10⁷	>18	>25	>1	>10/>0.5	>3.3
Maximum operating temperature (°C)	/	/	/	/	200	327
Array	/	3 × 3	3 × 3	4 × 4	5 × 3	3 × 3
Light wave- length (nm)	/	/	375	365, 405	365	405
Retention time (s)	>10⁵	>10³	>10⁴	>10²	/	>5 × 10² at 327 °C
Electrical energy consumption	32.25 pW/0.48 nW	/	0.55 fJ	/	/	/
Synaptic behaviors and Neural activities	EPSC, PPF, SDDP, SNDP, STDP, LTP, LTD	EPSC, PPF, SDDP, SNDP, SRDP, SVDP, LTP, LTD, Learning-forgetting-relearning	EPSC, PPF, SDDP, SNDP, SRDP, STM, LTM, Learning-forgetting-relearning	EPSC, PPF, SDDP, SNDP, SRDP, STDP, STM, LTM, Learning-forgetting-relearning, classical conditioning of Pavlov's dog	EPSC, PPF, SDDP, SNDP, SVDP, LTP, LTD, Learning-forgetting-relearning, classical conditioning of Pavlov's dog	EPSC, PPF, SDDP, SNDP, SRDP, Learning-forgetting
Neuromorphic applications	Nociceptor	Image learning and memory	Image learning and memory	Wireless transmission, MNIST handwritten digit recognition	Image learning and memory	Image learning and memory, color quantization

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Received: 15 November 2024 Revised: 30 December 2024 Online: Accepted Manuscript: 06 January 2025Uncorrected proof: 22 January 2025Published: 15 February 2025

Article Navigation > Journal of Semiconductors > 2025 > 46(2): 021403

Boyu Ye, Xiao Liu, Chao Wu, Wensheng Yan, Xiaodong Pi. Synaptic devices based on silicon carbide for neuromorphic computing[J]. Journal of Semiconductors, 2025, 46(2): 021403. doi: 10.1088/1674-4926/24100020 ****B Y Ye, X Liu, C Wu, W S Yan, and X D Pi, Synaptic devices based on silicon carbide for neuromorphic computing[J]. J. Semicond., 2025, 46(2), 021403 doi: 10.1088/1674-4926/24100020

Citation:

Boyu Ye, Xiao Liu, Chao Wu, Wensheng Yan, Xiaodong Pi. Synaptic devices based on silicon carbide for neuromorphic computing[J]. Journal of Semiconductors, 2025, 46(2): 021403. doi: 10.1088/1674-4926/24100020 ****

B Y Ye, X Liu, C Wu, W S Yan, and X D Pi, Synaptic devices based on silicon carbide for neuromorphic computing[J]. J. Semicond., 2025, 46(2), 021403 doi: 10.1088/1674-4926/24100020

Citation:

B Y Ye, X Liu, C Wu, W S Yan, and X D Pi, Synaptic devices based on silicon carbide for neuromorphic computing[J]. J. Semicond., 2025, 46(2), 021403 doi: 10.1088/1674-4926/24100020

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Synaptic devices based on silicon carbide for neuromorphic computing

DOI: 10.1088/1674-4926/24100020

CSTR: 32376.14.1674-4926.24100020

Boyu Ye¹,
Xiao Liu^{1, 5,},
Chao Wu⁴,
Wensheng Yan¹,
Xiaodong Pi^{2, 3,}

1.
Institute of Carbon Neutrality and New Energy, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
2.
State key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China
3.
Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311200, China
4.
Sorbonne Université, Faculté des Sciences, CNRS, Institut Parisien de Chimie Moléculaire (IPCM), UMR 8232, 4 Place Jussieu, 75005 Paris, France
5.
State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610065, China

More Information

Boyu Ye is now an undergraduate student at the School of Electronics and Information, Hangzhou Dianzi University under the supervision of Dr. Xiao Liu and prof. Liang Chu. His research focuses on synaptic devices for neuromorphic computing
Xiao Liu got her PhD degree in 2021 at Sorbonne University in France. Then she joined the group of academician Deren Yang and Prof. Xiaodong Pi at Zhejiang University as a postdoctor. In July 2023, she joined Hangzhou Dianzi University as a lecturer. Her research interests include inorganic/organic optoelectronic synaptic devices and their neuromorphic applications
Xiaodong Pi received his PhD degree at the University of Bath in 2004. He then carried out research at McMaster University and the University of Minnesota at Twin Cities. He joined Zhejiang University as an associate professor in 2008. He is now a professor in the State Key Laboratory of Silicon and Advanced Semiconductor Materials, the School of Materials Science and Engineering, and Zhejiang University-Hangzhou Global Scientific and Technological Innovation Center. His research mainly concerns group Ⅳ semiconductor materials and devices
Corresponding author: liuxiao@hdu.edu.cn; xdpi@zju.edu.cn
Received Date: 2024-11-15
Revised Date: 2024-12-30

Available Online: 2025-01-06

Abstract

Abstract

To address the increasing demand for massive data storage and processing, brain-inspired neuromorphic computing systems based on artificial synaptic devices have been actively developed in recent years. Among the various materials investigated for the fabrication of synaptic devices, silicon carbide (SiC) has emerged as a preferred choices due to its high electron mobility, superior thermal conductivity, and excellent thermal stability, which exhibits promising potential for neuromorphic applications in harsh environments. In this review, the recent progress in SiC-based synaptic devices is summarized. Firstly, an in-depth discussion is conducted regarding the categories, working mechanisms, and structural designs of these devices. Subsequently, several application scenarios for SiC-based synaptic devices are presented. Finally, a few perspectives and directions for their future development are outlined.
- silicon carbide,
- wide bandgap semiconductors,
- synaptic devices,
- neuromorphic computing,
- high temperature

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