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High hall mobility carbonized steamed buns-based polymer memristor for neuromorphic computing and image recognition

Chenjian Zhang1, Jiaxuan Liu1, Dongliang Zhang2, , Tianhao Qin1, Kexin Wang3, , Haidong He2 and Yu Chen1,

+ Author Affiliations

 Corresponding author: Dongliang Zhang, chinahhd@sina.com; Kexin Wang, 13847664896@163.com; Yu Chen, 3148704620@qq.com

DOI: 10.1088/1674-4926/25120015CSTR: 32376.14.1674-4926.25120015

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Abstract: The development of new n-type semiconductors is crucial for the further advancement of electronic and optoelectronic devices. Steamed buns, anciently known as "man tou", mainly made of wheat flour and are one of the staple foods for Chinese people. After being subjected to high-temperature treatment, the steamed buns transformed into carbonized steamed buns (CSB) with porous nanostructures, which exhibit a Hall mobility of up to 1.62 cm2/(V·s), far greater than C60(1.5×10−3~2.5×10−2 cm2/(V·s)), PCBM (2.0×10−7 cm2/(V·s)) and many polymer semiconductors (~10−6~10−2 cm2/(V·s)). A CSB-based bulk heterojunction memristor with a configuration of ITO/the CSB: PVK blends/Al is successfully fabricated. The device shows outstanding history dependent memristive switching performance, with 35 distinguishable conductance states, at a small sweep voltage range of ±1V. An achieved production yield reaches up to 89%. Upon being subjected to consecutive positive or negative voltage sweeps, the current flowing through the device can be modulated continuously. When the 15 consecutive pulse voltages (pulse amplitude: 0.1 V; pulse width:10 μs, pulse period: 20 μs) were applied to the device, the observed total power consumption was about 7.63 nJ, suggesting a potential in low-energy neuromorphic computing applications. As expected, both the CSB and PVK do not exhibit any memristive effect under the same experimental condition. Utilizing the characteristic that the device can linearly adjust the weights, a simple convolutional neural network for traffic sign recognition was successfully constructed. After 300 rounds of training, the achieved recognition accuracy rate reached 88.77%. This work not only provides a new approach for developing low-cost and readily available organic semiconductors with high Hall mobility, but also offers a new idea for the subsequent development of high-performance artificial synapses and optoelectronic devices using carbonized steamed buns.

Key words: carbonized steamed bunshall mobilitybulk heterojunction memristorartificial synapsestraffic sign recognition



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Che Q, Liu J X, Chen Q, et al. Polyfluorene conjugated polyelectrolytes–covalently modified black phosphorus nanosheets for bioinspired electronics. ACS Appl Mater Interfaces, 2024, 16(16): 19947-19956 doi: 10.1021/acsami.3c13015.s001
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Yao P, Wu H Q, Gao B, et al. Fully hardware-implemented memristor convolutional neural network. Nature, 2020, 577(7792): 641 doi: 10.1038/s41586-020-1942-4
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Dong W J, Ji X, An C B, et al. Harnessing conversion bridge strategy by organic semiconductor in polymer matrix memristors for high-performance multi-modal neuromorphic signal processing. InfoMat, 2025, 7(5): e12659 doi: 10.1002/inf2.12659
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[25]
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[27]
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[28]
Bousoulas P, Sakellaropoulos D, Tsoukalas D. Tuning the analog synaptic properties of forming free SiO2 memristors by material engineering. Appl Phys Lett, 2021, 118(14): 143502 doi: 10.1063/5.0044647
Fig. 1.  (Color online) Schematic diagram of the device fabrication process.

Fig. 2.  (Color online) (a) Schematic diagram of preparing carbonized steamed buns; (b) a comparison of the physical appearance of the products before and after carbonization of the steamed buns; low-resolution (c) and (d) high-resolution SEM images (scale bar: 100 μm), high-resolution TEM image (e) and XRD pattern (f) of CSB; (g) cyclic voltammetry curve of the CSB coated on Pt disk electrode at room temperature. Scan rate: 100 mV/s; reference electrode: Ag/Ag+; Electrolyte: recrystallized tetra-n-butylammonium-hexafluorophosphate (TBAPF6, 0.1 M) in deaerated acetonitrile. (h) light-induced electron paramagnetic resonance spectra of the CSB: PVK blends (weight ratio of 1 : 1) before and after illumination with 365 nm laser (12 W, irradiation time: 5 m).

Fig. 3.  (Color online) (a) The current-voltage characteristics of the Al/CSB:PVK/ITO device in a sweeping range of ±1 V; (b) retention performance of 35 conductance states obtained under different sweep numbers in a sweep voltage range of ± 1 V (read at 0.2 V); (c) the device current changes under the stimulation of continuous potentiation and depression (pulse amplitude: ±1 V; pulse duration: 10 ms; pulse period: 1s; Monitoring voltage: 0.2 V); (d) Evolution of the device current with 20 voltage pulse stimulations at different frequencies; (e) the absolute value of current change (|Δ(In−I1)|) versus pulse interval time and the voltage pulse numbers for the Al/CSB:PVK/ITO device; (f)Plasticity versus pulse interval characteristics of the device. The fitting of PPF and PTP are according to the equation of y = y0 +A1exp(−x/t1) + A2exp(−x/t2). The amplitude and duration of the voltage pulses are 1 V and 10 ms, respectively. The current responses are monitored with a small voltage of 0.2 V; (g) The synaptic weight versus the time interval between the two pulses. A pair of identical negative voltage pulses (−2 v, 20 ms) were applied to the top and bottom electrodes of the device, respectively. Power consumption and current-time curves of the Al/CSB: PVK/ITO device under (h) one and (i) 15 stimulations of continuous pulsed potentiation. The amplitude, duration, and period of the voltage pulses are 0.2 V, 30 μs, and 90 μs, respectively.

Fig. 4.  (Color online) (a) The current changes under different pulse voltages. For each measurement, the pulse numbers applied to the device are 15; (b) the changes of device plasticity as a function of the pulse amplitudes; (c) current changes observed at different numbers of the same voltage pulse stimulations (pulse amplitude: 2 V; pulse duration: 10 ms; pulse period: 90 ms); (d) evolution of the relaxation time constant and the stabilized synaptic weight along with the stimulating pulse numbers.

Fig. 5.  (Color online) (a) Convolutional neural network (CNN) architecture for traffic sign recognition; the normalized distribution of the device conductance values after continuously applying 50 enhancement pulses (b) and 50 suppression pulses (c); (d) a database containing eight types of traffic signs; (e) the confusion matrix results for identifying traffic signs; (f) the accuracy statistics chart for each category; (g) the changes of the loss value with the training cycle;(h) the changes of recognition accuracy with the training period.

[1]
Steyrleuthner R, Schubert M, Jaiser F, et al. Bulk electron transport and charge injection in a high mobility n-type semiconducting polymer. Adv Mater, 2010, 22(25): 2799 doi: 10.1002/adma.201000232
[2]
Chen J D, Zhang W F, Wang L P, et al. Recent research progress of organic small-molecule semiconductors with high electron mobilities. Adv Mater, 2023, 35(11): 2210772 doi: 10.1002/adma.202210772
[3]
Okamoto T, Kumagai S, Fukuzaki E, et al. Robust, high-performance n-type organic semiconductors. Sci Adv, 2020, 6(18): eaaz0632 doi: 10.1126/sciadv.aaz0632
[4]
Chen X N, Li Q B, Liu Y M, et al. Dibenzothiophene sulfone-based n-type emissive organic semiconductor. J Mater Chem C, 2025, 13(26): 13448 doi: 10.1039/D5TC00969C
[5]
Xu Y, Xu C H, Wang C, et al. Degradable n-type organic semiconductors based on Knoevenagel condensation reaction. Adv Funct Mater, 2024, 34(34): 2400774 doi: 10.1002/adfm.202400774
[6]
Armin A, Chen Z M, Jin Y C, et al. A Shockley-type polymer: Fullerene solar cell. Adv Energy Mater, 2018, 8(7): 1701450
[7]
Foster S, Deledalle F, Mitani A, et al. Electron collection as a limit to polymer: PCBM solar cell efficiency: Effect of blend microstructure on carrier mobility and device performance in PTB7: PCBM. Adv Energy Mater, 2014, 4(14): 1400311 doi: 10.1002/aenm.201400311
[8]
L Lin Y Z, Wang J Y, Zhang Z G, et al. An electron acceptor challenging fullerenes for efficient polymer solar cells. Adv Mater, 2015, 27(7): 1170 doi: 10.1002/adma.201404317
[9]
McDowell C, Bazan G C. Organic solar cells processed from green solvents. Curr Opin Green Sustain Chem, 2017, 5: 49 doi: 10.1016/j.cogsc.2017.03.007
[10]
Al-Bataineh S A, Britcher L G, Griesser H J. Rapid radiation degradation in the XPS analysis of antibacterial coatings of brominated furanones. Surf Interface Anal, 2006, 38(11): 1512 doi: 10.1002/sia.2387
[11]
Wang J T, Takashima S, Wu H C, et al. Donor–acceptor poly(3-hexylthiophene)-block-pendent poly(isoindigo) with dual roles of charge transporting and storage layer for high-performance transistor-type memory applications. Adv Funct Mater, 2016, 26(16): 2695
[12]
Bin H J, Gao L, Zhang Z G, et al. 11. 4% Efficiency non-fullerene polymer solar cells with trialkylsilyl substituted 2D-conjugated polymer as donor. Nat Commun, 2016, 7: 13651
[13]
Zhang B, Chen Y, Ren Y J, et al. In situ synthesis and nonvolatile rewritable-memory effect of polyaniline-functionalized graphene oxide. Chem, 2013, 19(20): 6265
[14]
Fan F, Zhang B, Cao Y M, et al. Conjugated polymer covalently modified graphene oxide quantum dots for ternary electronic memory devices. Nanoscale, 2017, 9(30): 10610 doi: 10.1039/C7NR02809A
[15]
Brabec C J, Cravino A, Meissner D, et al. Origin of the open circuit voltage of plastic solar cells. Adv Funct Mater, 2001, 11(5): 374 doi: 10.1002/1616-3028(200110)11:5<374::AID-ADFM374>3.0.CO;2-W
[16]
Heeger A J. 25th anniversary article: Bulk heterojunction solar cells: Understanding the mechanism of operation. Adv Mater, 2014, 26(1): 10 doi: 10.1002/adma.201304373
[17]
Dyakonov V. The polymer–fullerene interpenetrating network: One route to a solar cell approach. Phys E Low Dimension Syst Nanostruct, 2002, 14(1-2): 53 doi: 10.1016/S1386-9477(02)00359-4
[18]
Li Y X, Gu M C, Pan Z, et al. Indacenodithiophene: A promising building block for high performance polymer solar cells. J Mater Chem A, 2017, 5(22): 10798 doi: 10.1039/C7TA02562A
[19]
Gu M C, Zhao Z Z, Wang K X, et al. Optoelectrical switching of nonfullerene acceptor Y6 and BPQD-based bulk heterojunction memory device through photoelectric effect. Adv Electron Mater, 2021, 7(4): 2001191 doi: 10.1002/aelm.202001191
[20]
Li J Y, Hou J, Zhang B, et al. Photoelectric dual response nonvolatile memory device based on black phosphorus quantum dots and fullerene derivative composite. Adv Electron Mater, 2022, 8(2): 2101143 doi: 10.1002/aelm.202101143
[21]
Che Q, Liu J X, Chen Q, et al. Polyfluorene conjugated polyelectrolytes–covalently modified black phosphorus nanosheets for bioinspired electronics. ACS Appl Mater Interfaces, 2024, 16(16): 19947-19956 doi: 10.1021/acsami.3c13015.s001
[22]
Yao P, Wu H Q, Gao B, et al. Fully hardware-implemented memristor convolutional neural network. Nature, 2020, 577(7792): 641 doi: 10.1038/s41586-020-1942-4
[23]
Dong W J, Ji X, An C B, et al. Harnessing conversion bridge strategy by organic semiconductor in polymer matrix memristors for high-performance multi-modal neuromorphic signal processing. InfoMat, 2025, 7(5): e12659 doi: 10.1002/inf2.12659
[24]
Jang J W, Park S, Burr G W, et al. Optimization of conductance change in Pr1–xCaxMnO3-Based synaptic devices for neuromorphic systems. IEEE Electron Device Lett, 2015, 36(5): 457 doi: 10.1109/LED.2015.2418342
[25]
Jang J W, Park S, Jeong Y H, et al. ReRAM-based synaptic device for neuromorphic computing. 2014 IEEE International Symposium on Circuits and Systems (ISCAS). Melbourne, VIC, Australia. IEEE, 2014: 1054
[26]
Park S M, Hwang H G, Woo J U, et al. Improvement of conductance modulation linearity in a Cu2+-doped KNbO3 memristor through the increase of the number of oxygen vacancies. ACS Appl Mater Interfaces, 2020, 12(1): 1069 doi: 10.1021/acsami.9b18794
[27]
Querlioz D, Dollfus P, Bichler O, et al. Learning with memristive devices: How should we model their behavior? 2011 IEEE/ACM International Symposium on Nanoscale Architectures. San Diego, CA, USA. IEEE, 2011, 150
[28]
Bousoulas P, Sakellaropoulos D, Tsoukalas D. Tuning the analog synaptic properties of forming free SiO2 memristors by material engineering. Appl Phys Lett, 2021, 118(14): 143502 doi: 10.1063/5.0044647

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    Received: 05 December 2025 Revised: 07 January 2026 Online: Accepted Manuscript: 13 January 2026Uncorrected proof: 16 January 2026

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      Chenjian Zhang, Jiaxuan Liu, Dongliang Zhang, Tianhao Qin, Kexin Wang, Haidong He, Yu Chen. High hall mobility carbonized steamed buns-based polymer memristor for neuromorphic computing and image recognition[J]. Journal of Semiconductors, 2026, In Press. doi: 10.1088/1674-4926/25120015 ****C J Zhang, J X Liu, D L Zhang, T H Qin, K X Wang, H D He, and Y Chen, High hall mobility carbonized steamed buns-based polymer memristor for neuromorphic computing and image recognition[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/25120015
      Citation:
      Chenjian Zhang, Jiaxuan Liu, Dongliang Zhang, Tianhao Qin, Kexin Wang, Haidong He, Yu Chen. High hall mobility carbonized steamed buns-based polymer memristor for neuromorphic computing and image recognition[J]. Journal of Semiconductors, 2026, In Press. doi: 10.1088/1674-4926/25120015 ****
      C J Zhang, J X Liu, D L Zhang, T H Qin, K X Wang, H D He, and Y Chen, High hall mobility carbonized steamed buns-based polymer memristor for neuromorphic computing and image recognition[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/25120015

      High hall mobility carbonized steamed buns-based polymer memristor for neuromorphic computing and image recognition

      DOI: 10.1088/1674-4926/25120015
      CSTR: 32376.14.1674-4926.25120015
      More Information
      • Chenjian Zhang was born in Anhui, China in 2000 and received his B.S. degree from the School of Materials and Chemical Engineering, Chuzhou University, China, in 2018. He then joined Prof. Dr Yu Chen’s group as a PhD student at the same year. Up to date he has published several papers in the international peer-review journals. His research interests mainly concern the design and synthesis of organic and polymeric functional materials for optoelectronic memristors
      • Yu Chen is a chair professor at East China University of Science and Technology in Shanghai. He received his Ph.D. in Organic Chemistry at Fudan University in July 1996. Starting from February 2000, he took four-month German language Course in Geothe Institute at Schwaebisch Hall. He then joined Prof. Michael Hanack's group at University of Tuebingen, as an Alexander von Humboldt research fellow. At the end of October 2002, he moved to the University of Washington at Seattle, USA, and worked with Prof. Alex K. Y. Jen as a research associate. In February 2004, he joined the Professor Osamu Ito’s group at Tohoku University, Japan as a research scientist of CREST, JST. He has published more than 300 articles in the peer-review journals such as Nat. Commun., Adv. Sci., Angew. Chem. Int. Ed., J. Am. Chem. Soc., Adv. Mater. and others. His research interests mainly concern the design and synthesis of organic and polymeric functional materials for optoelectronics and memristors
      • Corresponding author: chinahhd@sina.com13847664896@163.com3148704620@qq.com
      • Received Date: 2025-12-05
      • Revised Date: 2026-01-07
      • Available Online: 2026-01-13

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