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Perovskite-Enhanced Silicon-Nanocrystal Optoelectronic Synaptic Devices for the Simulation of Biased and Correlated Random-Walk Learning
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Yiyue Zhu1, Wen Huang1, Yifei He1, Lei Yin1, Yiqiang Zhang2, Deren Yang1, Xiaodong Pi1, 3
Research. Vol 2020 Article ID 7538450
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Research. Vol 2020 Article ID 7538450
• Research Article •
Perovskite-Enhanced Silicon-Nanocrystal Optoelectronic Synaptic Devices for the Simulation of Biased and Correlated Random-Walk Learning
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Yiyue Zhu1, Wen Huang1, Yifei He1, Lei Yin1, Yiqiang Zhang2, Deren Yang1, Xiaodong Pi1, 3
Affiliations
  • 1State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
  • 2School of Materials Science and Engineering, Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, Henan 450001, China
  • 3Institute of Advanced Semiconductors, Hangzhou Innovation Center, Zhejiang University, Hangzhou, Zhejiang 311215, China
About Author:

Authors’ Contributions

Yiyue Zhu and Xiaodong Pi conceived the idea and designed the experiments. Yiyue Zhu and Wen Huang conducted the experiments. Yifei He did the FDTD simulation. Lei Yin and Yiqiang Zhang participated in the discussion of results. Yiyue Zhu and Xiaodong Pi wrote the manuscript, which was reviewed by all the authors.

Published: 2020-09-20 doi: 10.34133/2020/7538450
Outline
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Silicon- (Si-) based optoelectronic synaptic devices mimicking biological synaptic functionalities may be critical to the development of large-scale integrated optoelectronic artificial neural networks. As a type of important Si materials, Si nanocrystals (NCs) have been successfully employed to fabricate optoelectronic synaptic devices. In this work, organometal halide perovskite with excellent optical asborption is employed to improve the performance of optically stimulated Si-NC-based optoelectronic synaptic devices. The improvement is evidenced by the increased optical sensitivity and decreased electrical energy consumption of the devices. It is found that the current simulation of biological synaptic plasticity is essentially enabled by photogating, which is based on the heterojuction between Si NCs and organometal halide perovskite. By using the synaptic plasticity, we have simulated the well-known biased and correlated random-walk (BCRW) learning.

Yiyue Zhu, Wen Huang, Yifei He, Lei Yin, Yiqiang Zhang, Deren Yang, Xiaodong Pi. Perovskite-Enhanced Silicon-Nanocrystal Optoelectronic Synaptic Devices for the Simulation of Biased and Correlated Random-Walk Learning[J]. Research, 2020 , 2020 (9) : 7538450 - . DOI: 10.34133/2020/7538450
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1. 　Introduction
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Computers have gained worldwide popularity over the past few decades. The von Neumann architecture on which computers are based, however, has been increasingly limiting the further development of computers [1–4]. Nowadays, an artificial neural network (ANN) is regarded as one of the most important alternative architectures for computers in the future since neuromorphic computing based on the ANN is capable of parallelly processing information and executing brain-like operations such as learning and memorizing with ultralow energy consumption [5–13]. For the construction of an ANN, artificial synapses (i.e., synaptic devices) are critical given the fact that information transmitted among neurons basically relies on synapses in a biological neural system [14, 15]. Until now, different kinds of synaptic devices have been proposed by using various functional materials such as metal oxide films [16–23], organic films [24, 25], two-dimensional layered films [26–30], and semiconductor nanostructures [31–35]. It is noteworthy that silicon nanocrystals (Si NCs) have been successfully employed in the fabrication of synaptic devices as a type of important Si materials [36–39]. They exemplified the great promise for the development of Si-based optoelectronically integrated ANNs, which would facilitate widely deployable neuromorphic computing.
An optoelectronic synaptic device is usually stimulated by presynaptic optical spikes [36]. Under a bias, the device exhibits optical-spike-induced postsynaptic current, consuming electrical energy. High optical sensitivity and low electrical energy consumption are highly demanded by an optoelectronic synaptic device [37, 40, 41]. Hence, significant efforts for rendering Si-NC-based optoelectronic synaptic devices better optical sensitivity and lower electrical energy consumption are well justified. It is known that organometal halide perovskite is emerging as an excellent optoelectronic material [15, 42–46]. A pronounced feature of the optical properties of organometal halide perovskite is its extremely strong optical absorption [47]. This inspires the route of hybridizing Si NCs with organometal halide perovskite to enhancing the optical sensitivity of Si-NC-based optoelectronic synaptic devices. The electronic coupling between organometal halide perovskite and Si NCs also likely helps to reduce the electrical energy consumption of Si-NC-based optoelectronic synaptic devices.
In this work, an organometal halide perovskite film is coated on a Si NC film, which is the channel of an optoelectronic synaptic transistor. It turns out that the organometal halide perovskite film not only significantly improves the optical sensitivity of the device but also reduces the electrical energy consumption of the device. Synaptic functionalities, for instance excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), spike-number-dependent plasticity (SNDP), and spike-rate-dependent plasticity (SRDP) [48], can be all mimicked by using synaptic transistors which are based on the hybrid structure of organometal halide perovskite and Si NCs. Moreover, it is demonstrated that biased and correlated random-walk (BCRW) learning can be simulated by our synaptic devices.
2. 　Results and Discussion
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Figure 1(a) shows the photograph of a Si NC solution, in which Si NCs are dispersed in ethanol to render a concentration of ~10 mg/ml. The good dispersion of Si NCs is enabled by their heavy B doping [49]. The low-resolution transmission electron microscopy (TEM) image of Si NCs is shown in Figure 1(b) together with the high-resolution TEM image of an individual Si NC as the inset. Statistical analysis on the size of Si NCs indicates a mean size of ~11.2 nm and a standard deviation of ~2.4 nm (Figure 1(c)).
Optoelectronic synaptic devices with the structure schematically illustrated in Figure 1(d) are fabricated by employing the above-mentioned Si NCs. The Si NC solution is first spin-coated on SiO2/Si with Cr/Au electrodes. The resulting Si NC film in the area (10  μm long and 120  μm wide) between Cr/Au electrodes acts as the conducting material of an optoelectronic synaptic device. A film of the perovskite of MAPbI3 is then formed on the top of the Si NC film. From the cross-section scanning electron microscopy (SEM) image (Figure 1(e)), it is found that the perovskite film and Si NC film are ~500 nm and ~850 nm thick, respectively. The bilayer structure may be further clarified by energy dispersive spectrometer (EDS) line scanning for the element of Pb. A finite difference time domain (FDTD) method is used to analyze the optical absorption of the device for light with the wavelength of 532 nm incident on the top (i.e., perovskite). The cross-section distribution of the ratio of absorbed power per unit volume to total incident power of the synaptic device is shown in Figure 1(f). The perovskite film is found to be responsible for nearly 100% of the absorbed light. This is consistent with the fact that the absorption coefficient of a perovskite film is very large (~105 cm-1 at 532 nm) [47].
Figure 2(a) shows the EPSC of a synaptic device at 3 V induced by an optical spike with the wavelength of 532 nm, the duration of 200 ms, and the power density of 2  μW/cm2. The maximum value of EPSC (~0.12 nA) is reached just before the optical spike stops. The decay time (which is defined as the time required to reduce the EPSC from 90% to 10% of the original value) is measured to be ~0.4 s [50]. It is found that the EPSC increases as the duration of the optical spike increases, which is shown in Figure 2(b). The saturation of the EPSC occurs when the duration is larger than ~10 s. Such a behavior of the EPSC for the synaptic device is similar to that for a biological synapse [51]. Figure S1 shows the EPSC of twelve devices. The standard deviation of the EPSC is ~5.7% of the average EPSC, which is better than that reported by Yang et al. [52]. This indicates the good repeatability of our devices.
Figure 2(c) schematically illustrates the band alignment of the heterojunction formed between perovskite and Si NCs. Please note that Si NCs are p-type degenerated semiconductors [38, 53], while the perovskite of MAPbI3 is a n-type semiconductor [54, 55]. When an optical spike is incident on the top of the heterojunction, photo-generated holes and electrons appear in perovskite. The built-in electric field of the heterojunction allows photo-generated holes to move into Si NCs, while leaving photo-generated electrons in perovskite. As a result, the hole concentration in the Si NC film increases, giving rise to the EPSC. Since photo-generated holes transferred into Si NCs are physically separated from photo-generated electrons in perovskite, they have longer lifetime. Hence, we observe the slow decay of the EPSC. This is the so-called the photogating effect [55, 56].
In order to further clarify the working mechanism, we have fabricated a transistor only based on the Si NC film and a transistor only based on the perovskite film, as shown in Figure S2. It is found that the conductivity of the Si NC film is quite comparable to that of the perovskite film. Liu et al. [57] showed that for two layers with similar conductivity, the layer near the insulator plays a decisive role in the charge transport of a transistor. Therefore, the Si NC layer should be regarded as the conductive channel of a synaptic device in the current work. We have also compared the photocurrent of a Si NC synaptic device with that of a perovskite-enhanced Si NC synaptic device. The Si NC synaptic device has a negative photocurrent [37], while the perovskite-enhanced Si NC synaptic device has a large positive photocurrent. Such comparison indicates that under illumination, the carrier concentration in the perovskite-enhanced Si NC synaptic device is clearly larger than that of the Si NC synaptic device. Hence, it may be inferred that photo-generated holes in perovskite move to Si NCs, leading to the increase of the hole concentration of the Si NC conductive channel in the current work. This is in fact consistent with the band alignment of the heterojunction between perovskite and Si NCs (Figure 2(c)).
The sensitivity of an optoelectronic synaptic device to optical stimulation may be evaluated by responsivity (R), which is the ratio of the photocurrent (Iph) to the power of optical stimulation (P). In the current work, we obtain the highest responsivity of 18 A/W when the device is stimulated by an optical spike to exhibit the saturated EPSC. By comparing the current device (Figure S3) with a synaptic device only based on Si NCs under the illumination with the same power density, we find that the responsivity of the current device is more than three orders of magnitude larger than that of the synaptic device only based on Si NCs [37]. This indicates that perovskite with excellent optical absorption indeed enhances the sensitivity of a Si NC synaptic device. In addition, the electrical energy consumption per synaptic event for a synaptic device based on the hybrid structure of Si NCs and perovskite is calculated by using
(1)dE=V×I×dt,
in which V is the bias (3 V) and I is the current induced by a spike at the time of t [58]. It is found that the lowest electrical energy consumption of 0.9 nJ is obtained at the shortest duration of 200 ms in this work. As shown in Figure S4, a lower bias (e.g., 0.01 V) may be applied to the device to get even lower electrical energy consumption (~0.11 pJ), which is more than three orders of magnitude smaller than that of a Si NC synaptic device [37]. Therefore, we conclude that perovskite helps reduce the electrical energy consumption of a Si NC synaptic device.
Figure 3(a) shows the EPSC of a perovskite-enhanced Si NC synaptic device induced by two sequential optical spikes with a 200 ms interval time (Δt). We can find that the EPSC induced by the second optical spike (A2) is bigger than that induced by the first one (A1). This is the so-called PPF, which is a characteristic manifestation of STP in a biological neural system. If we define the PPF index as the ratio of A2 and A1, we can work out the dependence of the PPF index on Δt (Figure 3(b)). It is found that the PPF index decreases as Δt increases. When Δt is very small, the PPF index reaches about 130%. However, the PPF index tends to be 100% when Δt exceeds ~3 s. This change is consistent with the learning process enabled by biological synapses [23].
The EPSC induced by optical spikes with different spike quantities is shown in Figure 3(c). The quantity of optical spikes ranges from 2 to 20. It is clear that the EPSC increases when the quantity of optical spikes increases. Since the synaptic weight change (ΔW) can be calculated by using
(2)ΔW=An−A1/A1,
where An is the EPSC measured at the end of last optical spike. We may examine the dependence of ΔW on the quantity of spikes. It turns out that ΔW increases and then has a tendency to saturate at ~75% as the quantity of optical spikes increases (Figure 3(d)). Such a phenomenon may be called SNDP [48]. In addition, we find that SRDP also occurs to our perovskite-enhanced Si NC synaptic devices. Figure 3(e) shows that the EPSC of a perovskite-enhanced Si NC synaptic device increases when the optical spike frequency increases from 0.8 to 4.0 Hz. ΔW also increases and then saturates at ~81% as the spike frequency increases (Figure 3(f)).
Now we move to demonstrate that our perovskite-enhanced Si NC synaptic devices may be used to simulate practical learning. It is well known that biased and correlated random-walk (BCRW) learning is usually carried out by animals and humans [59–61]. Figure 4(a) shows an example of BCRW learning, in which a toddler learns to walk toward a target. The target is 500 units away from the starting point of the toddler, who walks one unit per step. The direction of each newly started step is evaluated by its angle (α) with respect to the horizontal line between the starting point and the target. α may randomly change from -90° to 90°. Since the range of choice of the absolute value of α (∣α∣) is closer to zero for better training in the BCRW learning, ∣α∣ may be correlated to learning times (n). n can be readily denoted by the quantity of optical spikes for a perovskite-enhanced Si NC synaptic device. Hence, we can write down
(3)α=180°/1+Sn−90°,
where Sn is given by the normalization of ΔWn with respect to its saturated value (i.e., ΔW20). We assume that the toddler totally walks 500 steps, reaching a final point denoted by (xf, yf). The values of xf and yf are obtained by using
(4)xf=∑500cosrandint90°−180°1+Sn,180°1+Sn−90°,yf=∑500sinrandint90°−180°1+Sn,180°1+Sn−90°,
where randinta,b randomly chooses a number between a and b. Figure 4(b) representatively shows the footprints of the toddler’s 500-step walk for n=1. The final point the toddler reaches is (314, 12). We define the distance between the final point and the target as D. The angel between the line of the starting point to the final point and that of the starting point to the target is θ. D and 2θ may be well used to evaluate the deviation of the final point with respect to the target.
Figure 4(c) shows the statistical results on the final points the toddler reaches after 1, 2, 5, or 20 times learning. It is clear that with the increase of training times, xf and yf more closely approach 500 and zero, respectively. The dependence of D and 2θ on the learning times is shown in Figures 4(d) and 4(e), respectively. When the learning times increases from 1 to 20, the average D (2θ) of 100 experiments of BCRWs decreases from 184 units (2.3°) to 0 (0). Such results mean that after more training, the toddler is able to walk to the target with less deviation, consistent with the biological model of BCRW learning [61]. Please note that the SNDP rather than a single perovskite-enhanced Si NC synaptic device is used to simulate the BCRW learning here. In order to take the full advantage of the SNDP through hardware implementation, perovskite-enhanced Si NC synaptic devices need to be interconnected with appropriate neuronal devices to form an ANN.
3. 　Conclusions
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In summary, we have incorporated perovskite into Si-NC-based optoelectronic synaptic devices. Synaptic functionalities such as EPSC, PPF, SNDP, and SRDP are simulated by these devices. It is found that perovskite clearly enhances the sensitivity of Si-NC-based synaptic devices to optical stimulation. Perovskite also lowers the electrical energy consumption of Si-NC-based synaptic devices. Significantly, we demonstrate that BCRW learning can be simulated by using the synaptic plasticity of perovskite-enhanced Si NC synaptic devices. The current work should inspire the development of novel optoelectronic devices based on the synergy of the traditional semiconductor of Si and the emerging semiconductors such as organometal halide perovskite.
4. 　Experimental Section
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4.1. 　Material Preparation
Si NCs doped with boron at the nominal concentration of ~40% were synthesized by nonthermal plasma. The flow rates of SiH4, B2H6, and Ar introduced into the nonthermal plasma chamber and the plasma pressure were controlled to obtain the desired size and dopant concentration for Si NCs. The nonthermal plasma was generated with the power of 140 W and the frequency of 13.56 MHz [53]. The synthesized Si NCs were then dispersed in ethanol forming a 10 mg/ml solution. Before the use of the Si NC solution, a three-minute ultrasonication with a cell pulverizer at the power of 90 W was carried out. CH3NH3I and PbI2 were mixed with the molar ratio of 1 : 1 and dissolved in a mixed solution of N,N-dimethylformamide (AR, 99.8%) and dimethyl sulfoxide (>99.9%) with the volume ratio of 4 : 1 [48].
4.2. 　Device Fabrication
We first deposited Cr/Au (10 nm/150 nm) electrodes on a silicon wafer with a 150 nm thick thermally grown SiO2 layer. The length and the width of the channel between the electrodes were 10 and 120  μm, respectively. The Si NC solution was then spin-coated on the silicon wafer at 1500 rpm for 60 s. The spin-coating was repeated a few times. After each spin-coating, the resulting Si NC film was annealed at 100°C for 5 minutes [36]. The perovskite layer was spin-coated on the Si NC film at 4000 rpm for 35 s. Antisolvent chlorobenzene (>99.9%) was drop-coated during the spin-coating of the perovskite layer. Finally, an annealing process of 100°C for 15 minutes was taken [48].
4.3. 　Characterization
A JEM 2100F transmission electron microscope with an acceleration voltage of 200 kV was used to obtain TEM images. A Zeiss GeminiSEM 500 scanning electron microscope was used to obtain the SEM images and EDS line scanning. A semiconductor parameter analyzer (FS480, PDA Co. Ltd.) was used to measure the optoelectronic synaptic devices. A Rigol DG5100 arbitrary function generator was used to modulate TTL-controlled optical shutters, rendering a series of optical spikes. All devices were measured after similar exposure to air. The FDTD simulation was performed by using the software of Lumerical Solutions Inc.
Funding
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  • Zhejiang University Education Foundation Global Partnership Fund
  • Natural Science Foundation of China for Innovative Research Groups(61721005)
  • Fundamental Research Funds for the Central Universities(2018XZZX003-02)
  • National Natural Science Foundation of China(61774133; 91964107)
  • National Key Research and Development Program of China(2018YFB2200101; 2017YFA0205704)
References
Less
1
Indiveri G., and Liu S.-C., “Memory and information processing in neuromorphic systems,” Proceedings of the IEEE, vol. 103, no. 8, pp. 1379–1397, 2015
2
Zidan M. A., Strachan J. P., and Lu W. D., “The future of electronics based on memristive systems,” Nature Electronics, vol. 1, no. 1, pp. 22–29, 2018
3
Li J., Ge C., Du J., Wang C., Yang G., and Jin K., “Reproducible ultrathin ferroelectric domain switching for high-performance neuromorphic computing,” Advanced Materials, vol. 32, no. 7, article 1905764, 2019
4
Jiang S., Nie S., He Y., Liu R., Chen C., and Wan Q., “Emerging synaptic devices: from two-terminal memristors to multiterminal neuromorphic transistors,” Materials Today Nano, vol. 8, article 100059, 2019
5
Tang J., Yuan F., Shen X., Wang Z., Rao M., He Y., Sun Y., Li X., Zhang W., Li Y., Gao B., Qian H., Bi G., Song S., Yang J. J., and Wu H., “Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges,” Advanced Materials, vol. 31, no. 49, article 1902761, 2019
6
Boybat I., le Gallo M., Nandakumar S. R., Moraitis T., Parnell T., Tuma T., Rajendran B., Leblebici Y., Sebastian A., and Eleftheriou E., “Neuromorphic computing with multi-memristive synapses,” Nature Communications, vol. 9, no. 1, article 2514, 2018
7
Furber S., “Large-scale neuromorphic computing systems,” Journal of Neural Engineering, vol. 13, no. 5, article 051001, 2016
8
Xia Q., and Yang J. J., “Memristive crossbar arrays for brain-inspired computing,” Nature Materials, vol. 18, no. 4, pp. 309–323, 2019
9
Huang H.-M., Yang R., Tan Z. H., He H. K., Zhou W., Xiong J., and Guo X., “Quasi-Hodgkin-Huxley neurons with leaky Integrate‐and‐Fire functions physically realized with memristive devices,” Advanced Materials, vol. 31, no. 3, article 1803849, 2018
10
Guo L. Q., Han H., Zhu L. Q., Guo Y. B., Yu F., Ren Z. Y., Xiao H., Ge Z. Y., and Ding J. N., “Oxide neuromorphic transistors gated by polyvinyl alcohol solid electrolytes with ultralow power consumption,” ACS Applied Materials & Interfaces, vol. 11, no. 31, pp. 28352–28358, 2019
11
Choi S., Tan S. H., Li Z., Kim Y., Choi C., Chen P. Y., Yeon H., Yu S., and Kim J., “SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations,” Nature Materials, vol. 17, no. 4, pp. 335–340, 2018
12
Cai F., Correll J. M., Lee S. H., Lim Y., Bothra V., Zhang Z., Flynn M. P., and Lu W. D., “A fully integrated reprogrammable memristor-CMOS system for efficient multiply-accumulate operations,” Nature Electronics, vol. 2, no. 7, pp. 290–299, 2019
13
Sanchez Esqueda I., Yan X., Rutherglen C., Kane A., Cain T., Marsh P., Liu Q., Galatsis K., Wang H., and Zhou C., “Aligned carbon nanotube synaptic transistors for large-scale neuromorphic computing,” ACS Nano, vol. 12, no. 7, pp. 7352–7361, 2018
14
Drachman D. A., “Do we have brain to spare?,” Neurology, vol. 64, no. 12, pp. 2004–2005, 2005
15
Xu W., Cho H., Kim Y.-H., Kim Y.-T., Wolf C., Park C.-G., and Lee T.-W., “Organometal halide perovskite artificial synapses,” Advanced Materials, vol. 28, no. 28, pp. 5916–5922, 2016
16
Strukov D. B., Snider G. S., Stewart D. R., and Williams R. S., “The missing memristor found,” Nature, vol. 453, no. 7191, pp. 80–83, 2008
17
Prezioso M., Merrikh-Bayat F., Hoskins B. D., Adam G. C., Likharev K. K., and Strukov D. B., “Training and operation of an integrated neuromorphic network based on metal-oxide memristors,” Nature, vol. 521, no. 7550, pp. 61–64, 2015
18
Wang Z., Kang J., Yu Z., Fang Y., Ling Y., Cai Y., Huang R., and Wang Y., “Modulation of nonlinear resistive switching behavior of a TaOx-based resistive device through interface engineering,” Nanotechnology, vol. 28, no. 5, article 055204, 2017
19
Zhou Y.-X., Li Y., Su Y. T., Wang Z. R., Shih L. Y., Chang T. C., Chang K. C., Long S. B., Sze S. M., and Miao X. S., “Nonvolatile reconfigurable sequential logic in a HfO2 resistive random access memory array,” Nanoscale, vol. 9, no. 20, pp. 6649–6657, 2017
20
Xue W., Li Y., Liu G., Wang Z., Xiao W., Jiang K., Zhong Z., Gao S., Ding J., Miao X., Xu X. H., and Li R. W., “Controllable and stable quantized conductance states in a Pt/HfOx/ITO memristor,” Advanced Electronic Materials, vol. 6, no. 2, article 1901055, 2020
21
Pan R., Li J., Zhuge F., Zhu L., Liang L., Zhang H., Gao J., Cao H., Fu B., and Li K., “Synaptic devices based on purely electronic memristors,” Applied Physics Letters, vol. 108, no. 1, article 013504, 2016
22
Li H. K., Chen T. P., Liu P., Hu S. G., Liu Y., Zhang Q., and Lee P. S., “A light-stimulated synaptic transistor with synaptic plasticity and memory functions based on InGaZnOx–Al2O3 thin film structure,” Journal of Applied Physics, vol. 119, no. 24, article 244505, 2016
23
Yang Q., Lv D., Huang J., Chen J., Chen H., and Guo T., “Modulation of the plasticity of an all-metal oxide synaptic transistor via laser irradiation,” Nanotechnology, vol. 31, no. 21, article 215202, 2020
24
Battistoni S., Peruzzi C., Verna A., Marasso S. L., Cocuzza M., Erokhin V., and Iannotta S., “Synaptic response in organic electrochemical transistor gated by a graphene electrode,” Flexible and Printed Electronics, vol. 4, no. 4, article 044002, 2019
25
van de Burgt Y., Lubberman E., Fuller E. J., Keene S. T., Faria G. C., Agarwal S., Marinella M. J., Alec Talin A., and Salleo A., “A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing,” Nature Materials, vol. 16, no. 4, pp. 414–418, 2017
26
Sun Y., Qian L., Xie D., Lin Y., Sun M., Li W., Ding L., Ren T., and Palacios T., “Photoelectric synaptic plasticity realized by 2D perovskite,” Advanced Functional Materials, vol. 29, no. 28, article 1902538, 2019
27
He H.-K., Yang R., Zhou W., Huang H. M., Xiong J., Gan L., Zhai T. Y., and Guo X., “Photonic potentiation and electric habituation in ultrathin memristive synapses based on monolayer MoS2,” Small, vol. 14, no. 15, article 1800079, 2018
28
Wang M., Cai S., Pan C., Wang C., Lian X., Zhuo Y., Xu K., Cao T., Pan X., Wang B., Liang S. J., Yang J. J., Wang P., and Miao F., “Robust memristors based on layered two-dimensional materials,” Nature Electronics, vol. 1, no. 2, pp. 130–136, 2018
29
Luo Z.-D., Xia X., Yang M.-M., Wilson N. R., Gruverman A., and Alexe M., “Artificial optoelectronic synapses based on ferroelectric field-effect enabled 2D transition metal dichalcogenide memristive transistors,” ACS Nano, vol. 14, no. 1, pp. 746–754, 2020
30
Jiang J., Hu W., Xie D., Yang J., He J., Gao Y., and Wan Q., “2D electric-double-layer phototransistor for photoelectronic and spatiotemporal hybrid neuromorphic integration,” Nanoscale, vol. 11, no. 3, pp. 1360–1369, 2019
31
Wang Y., Lv Z., Chen J., Wang Z., Zhou Y., Zhou L., Chen X., and Han S. T., “Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing,” Advanced Materials, vol. 30, no. 38, article 1802883, 2018
32
Shao L., Wang H., Yang Y., He Y., Tang Y., Fang H., Zhao J., Xiao H., Liang K., Wei M., Xu W., Luo M., Wan Q., Hu W., Gao T., and Cui Z., “Optoelectronic properties of printed photogating carbon nanotube thin film transistors and their application for light-stimulated neuromorphic devices,” ACS Applied Materials & Interfaces, vol. 11, no. 12, pp. 12161–12169, 2019
33
Li B., Wei W., Yan X., Zhang X., Liu P., Luo Y., Zheng J., Lu Q., Lin Q., and Ren X., “Mimicking synaptic functionality with an InAs nanowire phototransistor,” Nanotechnology, vol. 29, no. 46, article 464004, 2018
34
Li W., Liu X., Wang Y., Dai Z., Wu W., Cheng L., Zhang Y., Liu Q., Xiao X., and Jiang C., “Design of high-performance memristor cell using W-implanted SiO2 films,” Applied Physics Letters, vol. 108, no. 15, article 153501, 2016
35
Alibart F., Pleutin S., Guérin D., Novembre C., Lenfant S., Lmimouni K., Gamrat C., and Vuillaume D., “An organic nanoparticle transistor behaving as a biological spiking synapse,” Advanced Functional Materials, vol. 20, no. 2, pp. 330–337, 2010
36
Tan H., Ni Z., Peng W., Du S., Liu X., Zhao S., Li W., Ye Z., Xu M., Xu Y., Pi X., and Yang D., “Broadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing,” Nano Energy, vol. 52, pp. 422–430, 2018
37
Yin L., Han C., Zhang Q., Ni Z., Zhao S., Wang K., Li D., Xu M., Wu H., Pi X., and Yang D., “Synaptic silicon-nanocrystal phototransistors for neuromorphic computing,” Nano Energy, vol. 63, article 103859, 2019
38
Ni Z., Wang Y., Liu L., Zhao S., Xu Y., Pi X., and Yang D., “Hybrid structure of silicon nanocrystals and 2D WSe2 for broadband optoelectronic synaptic devices,” in 2018 IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, USA, December 2018, pp. 38.5.1–38.5.4
39
Zhao S., Ni Z., Tan H., Wang Y., Jin H., Nie T., Xu M., Pi X., and Yang D., “Electroluminescent synaptic devices with logic functions,” Nano Energy, vol. 54, pp. 383–389, 2018
40
Hu L., Fu S., Chen Y., Cao H., Liang L., Zhang H., Gao J., Wang J., and Zhuge F., “Ultrasensitive memristive synapses based on lightly oxidized sulfide films,” Advanced Materials, vol. 29, no. 24, article 1606927, 2017
41
Zhu X., and Lu W. D., “Optogenetics-Inspired tunable synaptic functions in memristors,” ACS Nano, vol. 12, no. 2, pp. 1242–1249, 2018
42
Alharbi E. A., Dar M. I., Arora N., Alotaibi M. H., Alzhrani Y. A., Yadav P., Tress W., Alyamani A., Albadri A., Zakeeruddin S. M., and Grätzel M., “Perovskite solar cells yielding reproducible photovoltage of 1.20 V,” Research, vol. 2019, article 8474698, pp. 1–9, 2019
43
Berhe T. A., Su W.-N., Chen C.-H., Pan C.-J., Cheng J.-H., Chen H.-M., Tsai M.-C., Chen L.-Y., Dubale A. A., and Hwang B.-J., “Organometal halide perovskite solar cells: degradation and stability,” Energy & Environmental Science, vol. 9, no. 2, pp. 323–356, 2016
44
Ahmadi M., Wu T., and Hu B., “A review on organic-inorganic halide perovskite photodetectors: device engineering and fundamental physics,” Advanced Materials, vol. 29, no. 41, article 1605242, 2017
45
Zhao X., Xu H., Wang Z., Lin Y., and Liu Y., “Memristors with organic-inorganic halide perovskites,” Info, vol. 1, pp. 183–210, 2019
46
Guo Q., Wu J., Yang Y., Liu X., Lan Z., Lin J., Huang M., Wei Y., Dong J., Jia J., and Huang Y., “High-performance and hysteresis-free perovskite solar cells based on rare-earth-doped SnO2Mesoporous scaffold,” Research, vol. 2019, article 4049793, pp. 1–13, 2019
47
De Wolf S., Holovsky J., Moon S. J., Löper P., Niesen B., Ledinsky M., Haug F. J., Yum J. H., and Ballif C., “Organometallic halide perovskites: sharp optical absorption edge and its relation to photovoltaic performance,” Journal of Physical Chemistry Letters, vol. 5, no. 6, pp. 1035–1039, 2014
48
Huang W., Hang P., Wang Y., Wang K., Han S., Chen Z., Peng W., Zhu Y., Xu M., Zhang Y., Fang Y., Yu X., Yang D., and Pi X., “Zero-power optoelectronic synaptic devices,” Nano Energy, vol. 73, article 104790, 2020
49
Zhou S., Ni Z., Ding Y., Sugaya M., Pi X., and Nozaki T., “Ligand-free, colloidal, and plasmonic silicon nanocrystals heavily doped with boron,” ACS Photonics, vol. 3, no. 3, pp. 415–422, 2016
50
Saidaminov M. I., Adinolfi V., Comin R., Abdelhady A. L., Peng W., Dursun I., Yuan M., Hoogland S., Sargent E. H., and Bakr O. M., “Planar-integrated single-crystalline perovskite photodetectors,” Nature Communications, vol. 6, no. 1, article 8724, 2015
51
Yang Y., and Lisberger S. G., “Purkinje-cell plasticity and cerebellar motor learning are graded by complex-spike duration,” Nature, vol. 510, no. 7506, pp. 529–532, 2014
52
Yang C.-S., Shang D. S., Liu N., Fuller E. J., Agrawal S., Talin A. A., Li Y. Q., Shen B. G., and Sun Y., “All-solid-state synaptic transistor with ultralow conductance for neuromorphic computing,” Advanced Functional Materials, vol. 28, no. 42, article 1804170, 2018
53
Ni Z., Ma L., Du S., Xu Y., Yuan M., Fang H., Wang Z., Xu M., Li D., Yang J., Hu W., Pi X., and Yang D., “Plasmonic silicon quantum dots enabled high-sensitivity ultrabroadband photodetection of graphene-based hybrid phototransistors,” ACS Nano, vol. 11, no. 10, pp. 9854–9862, 2017
54
Wang Q., Shao Y., Xie H., Lyu L., Liu X., Gao Y., and Huang J., “Qualifying composition dependentpandnself-doping in CH3NH3PbI3,” Applied Physics Letters, vol. 105, no. 16, article 163508, 2014
55
Yin L., Huang W., Xiao R., Peng W., Zhu Y., Zhang Y., Pi X., and Yang D., “Optically stimulated synaptic devices based on the hybrid structure of silicon nanomembrane and perovskite,” Nano Letters, vol. 20, no. 5, pp. 3378–3387, 2020
56
Fang H., and Hu W., “Photogating in low dimensional photodetectors,” Advancement of Science, vol. 4, no. 12, article 1700323, 2017
57
Liu C., and Sirringhaus H., “Polymer field-effect transistors based on semiconducting polymer heterojunctions,” Journal of Applied Physics, vol. 107, no. 1, article 014516, 2010
58
Li Y., Wang Y., Yang D., and Pi X., “Recent progress on optoelectronic synaptic devices,” Scientia Sinica Informationis, vol. 50, no. 6, pp. 892–912, 2020
59
Codling E. A., Plank M. J., and Benhamou S., “Random walk models in biology,” Journal of The Royal Society Interface, vol. 5, no. 25, pp. 813–834, 2007
60
Kareiva P. M., and Shigesada N., “Analyzing insect movement as a correlated random walk,” Oecologia, vol. 56, no. 2-3, pp. 234–238, 1983
61
Hausdorff J. M., Peng C. K., Ladin Z., Wei J. Y., and Goldberger A. L., “Is walking a random walk? Evidence for long-range correlations in stride interval of human gait,” Journal of Applied Physiology, vol. 78, no. 1, pp. 349–358, 1995
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Article Info
doi: 10.34133/2020/7538450
  • Receive Date：2020-05-03
  • Online Date：2026-10-27
  • Published：2020-09-20
Article Data
Affiliations
History
  • Received：2020-05-03
  • Accepted：2020-07-30
Funding
Zhejiang University Education Foundation Global Partnership Fund
Natural Science Foundation of China for Innovative Research Groups(61721005)
Fundamental Research Funds for the Central Universities(2018XZZX003-02)
National Natural Science Foundation of China(61774133; 91964107)
National Key Research and Development Program of China(2018YFB2200101; 2017YFA0205704)
Affiliations
    1State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
    2School of Materials Science and Engineering, Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, Henan 450001, China
    3Institute of Advanced Semiconductors, Hangzhou Innovation Center, Zhejiang University, Hangzhou, Zhejiang 311215, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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