Processing math: 100%
J. Semicond. > 2021, Volume 42 > Issue 6 > 064101

ARTICLES

Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing

Yujia Li1, 2, Jianshi Tang2, 3, , Bin Gao2, 3, Xinyi Li2, Yue Xi2, Wanrong Zhang1, He Qian2, 3 and Huaqiang Wu2, 3,

+ Author Affiliations

 Corresponding author: Jianshi Tang, jtang@tsinghua.edu.cn; Huaqiang Wu, wuhq@tsinghua.edu.cn

DOI: 10.1088/1674-4926/42/6/064101

PDF

Turn off MathJax

Abstract: Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing. In this paper, an oscillation neuron based on a low-variability Ag nanodots (NDs) threshold switching (TS) device with low operation voltage, large on/off ratio and high uniformity is presented. Measurement results indicate that this neuron demonstrates self-oscillation behavior under applied voltages as low as 1 V. The oscillation frequency increases with the applied voltage pulse amplitude and decreases with the load resistance. It can then be used to evaluate the resistive random-access memory (RRAM) synaptic weights accurately when the oscillation neuron is connected to the output of the RRAM crossbar array for neuromorphic computing. Meanwhile, simulation results show that a large RRAM crossbar array (> 128 × 128) can be supported by our oscillation neuron owing to the high on/off ratio (> 108) of Ag NDs TS device. Moreover, the high uniformity of the Ag NDs TS device helps improve the distribution of the output frequency and suppress the degradation of neural network recognition accuracy (< 1%). Therefore, the developed oscillation neuron based on the Ag NDs TS device shows great potential for future neuromorphic computing applications.

Key words: threshold switching, Ag nanodots, oscillation neuron, neuromorphic computing

A resistive random-access memory (RRAM)-based neural network has been extensively studied as a promising solution to overcome the von Neumann bottleneck faced in conventional artificial intelligence (AI) hardware[1-4]. As inspired by a biological neural network, an artificial neural network consists of synaptic and neuronal devices. In order to improve speed and power efficiency, the RRAM crossbar array, which can significantly accelerate the vector-matrix multiplication, has been developed to implement artificial synapses[5-8]. On the other hand, a neuronal device is needed at the end of each crossbar bit line (BL) to convert the weighted sum current of the analog RRAM synapses into spikes to transmit information to the next layer of neurons. Here integrate-and-fire neurons built with CMOS circuits are typically employed[9]. However, such complex CMOS neurons would occupy a much larger footprint than the BL pitch of the crossbar array, which causes serious column pitch matching problem[10, 11].

Recently, a compact oscillation neuron based on a metal–insulator transition (MIT) threshold switching (TS) device was proposed as a more scalable artificial neuron[12-16]. Compared to the CMOS neuron, an oscillation neuron has the benefits of small size and simple circuit structure, which is appealing for large-scale neuromorphic system applications. However, the on/off ratio of a typical MIT TS device is small (~102), which cannot be used for a large RRAM crossbar array (e.g., 12 × 1 array)[15]. Moreover, the high operation voltage of the MIT TS device may disturb the weights of the RRAM synapses and also increase the power consumption[16]. Alternatively, TS devices based on electrochemical metallization (ECM) filaments have attracted considerable attention due to their simple structure, large on/off ratio, and low operation voltage[17-23]. However, the uniformity of typical ECM TS devices is relatively poor, which may affect the accuracy of artificial neural networks. More recently, we have developed a high-uniformity HfO2-based TS device with patterned Ag nanodots (NDs) as the high-performance selector, which shows low leakage current (< 1 pA), high on/off ratio (> 108), and high endurance (> 108 cycles)[24].

In this work, we further implement an oscillation neuron using the HfO2/Ag NDs TS device. This neuron exhibits self-oscillation behavior at low applied voltage (1 V), where the oscillation frequency increases with the applied voltage and decreases with the load resistance. In addition, it can work with a large RRAM crossbar array (> 128 × 128) owing to the high on/off ratio (> 108) of Ag NDs TS device. Moreover, in the neural network simulation, a high recognition accuracy (loss < 1%) can be achieved by using this oscillation neuron because of its high uniformity.

Fig. 1(a) illustrates the schematic diagram of a simple artificial neural network. When the input voltages are applied to the crossbar synaptic array, the weighted sum currents are integrated by the neurons at the end of each column (BL) and trigger output spike firing when they reach the neuron thresholds. The circuit implementation of the oscillation neuron based on the TS device is shown in Fig. 1(b). The load resistance (RL) represents the RRAM synaptic weight connected in series with the TS device. Also, CL is the load capacitance including parallel capacitance and parasitic capacitance at the neuron node. Initially, the TS device is in the off state (Roff). When applying an input voltage pulse (Vin), the voltage mainly drops on the TS device since Roff > RL, and CL starts to charge. When Vin is larger than the threshold voltage of the TS device Vth, it turns to the on state (Ron), and then CL starts to discharge since the voltage drop on the TS device is reduced (Ron < RL). Once the voltage drop on the TS device is below the hold voltage of TS device Vhold, it switches back to Roff, ready for the next firing event. In this way, the TS device switches on and off between Ron and Roff, and the neuron outputs oscillation signal. If RL is chosen to satisfy Roff > > RL > > Ron, the ideal oscillation frequency f can be described as[13]:

Figure  1.  (Color online) (a) Schematic diagram of a typical artificial neural network. (b) Circuit implementation of the oscillation neuron with a TS device.
DownLoad: Full-Size Img PowerPoint
f=1/[RLCL×log(Vhold−VinVth−Vin)]. (1)

Eq. (1) shows a one-to-one correspondence between f and RL at a certain CL and Vin. Therefore, the oscillation frequency can be used to represent the weight of the RRAM synapse accurately.

In this study, we demonstrate the oscillation neuron using the Ag NDs TS device based on ECM filaments. The devices were fabricated with a cell size of 10 × 10 μm2. The bottom electrode was patterned by photolithography and deposited by sputtering of 5 nm Ti and 50 nm Pt. An 8 nm thick HfO2 dielectric was deposited by atomic layer deposition (ALD) at 250 °C. The Ag NDs with diameters of 50 nm were patterned by e-beam lithography (EBL) and deposited by sputtering. A 40 nm-thick Pt was deposited as the top electrode. The transmission electron microscope (TEM) image of the Ag NDs TS device is shown in Fig. 2(a). Fig. 2(b) exhibits the schematic illustration of the threshold switching process in the device. In the initial state, there is no conductive filament in the dielectric layer, and the device is in the high-resistance state (HRS). When applying a voltage (Vappl) larger than Vth, the electric field is locally enhanced in the areas with Ag NDs, so the Ag atoms are easier to be ionized as the ion source (Ag → Ag+ + e−) for diffusing toward the bottom electrode, which leads to the formation of metallic filaments and turns the device to a low-resistance state (LRS). In this device, the Ag filaments are thin and unstable due to the small amount of Ag ions. Spontaneous rupture of the filaments occurs immediately when Vappl goes below Vhold, which turns the device back to HRS. In this process, the Ag atoms form clusters on the trace of filaments. Owing to the highly ordered Ag NDs, Ag filaments tend to form at the same positions and the formed filaments would have similar morphology, in different operation cycles or different devices. Fig. 2(c) shows the typical current–voltage (I–V) curve of the Ag NDs TS device under voltage sweeps between 0 and 1 V. This device exhibits a low leakage current less than 1 pA and large selectivity over 108. The Vth and Vhold of Ag NDs TS device are 0.6 and 0.2 V, respectively, which are carefully tuned to work with the RRAM synapse. In order to evaluate the uniformity of the Ag NDs TS device, the distributions of Vth and Vhold are analyzed, and the cumulative probability of Vth and Vhold is shown in Fig. 2(d). The coefficient of variation is defined as CV = σ/μ to evaluate the variation, where μ and σ are the mean and standard deviation, respectively. This Ag NDs TS device exhibits excellent uniformity (CV < 10%) compared to other TS devices based on ECM filaments[24]. The endurance test of the Ag NDs TS device is shown in Fig. 2(e). In this measurement, the device is repeatedly turned on with SET pulses of Vset = 1 V and tset = 10 μs, and then relaxed to the off state, which is read with a small pulse of Vread = 0.1 V and tread = 10 μs. The on-state current (Ion) is limited to 10 μA. It is found that the Ag NDs TS device exhibits a high endurance of over 108 cycles. To implement an oscillation neuron, we connect the Ag NDs TS device in parallel with a load capacitance (CL) and then in series with a load resistor (RL) following the circuit configuration in Fig. 1(b). Fig. 2(f) shows the measured oscillation waveform of the neuron when RL = 50 kΩ, CL = 750 pF and Vin = 1 V. The test result shows that this oscillation neuron can output a continuous oscillation signal, when Vin, RL and CL are fixed. In addition, the Ag NDs TS oscillation neuron shows a certain time delay before its stable oscillation, which leads to a higher Vout in the first peak. It is owing to the turn-on delay time of the TS device[24]. The higher first peak may have an adverse effect on the synaptic weight precision by distorting the oscillation waveform, which can be minimized by further improving the TS device switching speed.

Figure  2.  (Color online) (a) TEM image of the Ag NDs TS device. (b) Schematic illustration of the threshold switching process in the device. (c) Typical current–voltage (I–V) curves for the Ag NDs TS device. (d) Cumulative probability of Vth and Vhold distributions for the Ag NDs TS device. (e) Endurance test of the Ag NDs TS device with over 108 cycles. (f) Measured oscillation waveform of the oscillation neuron.
DownLoad: Full-Size Img PowerPoint

More systematic studies on the oscillation neuron characteristics are performed as shown in Fig. 3. Fig. 3(a) shows the oscillation waveforms at different input pulse voltages when RL = 50 kΩ and CL = 750 pF. The oscillation frequencies are 31.6, 40.2, 46.9 and 51.4 kHz for Vin = 1, 1.2, 1.4, and 1.6 V, respectively. It is found that the oscillation frequency increases as a function of the pulse amplitude of Vin as described in Eq. (1), and shows good consistency with the simulation results, as shown in Fig. 3(b). Similarly, Fig. 3(c) shows the oscillation waveforms of the Ag NDs TS devices with different RL values (i.e., different synaptic weights) when Vin = 1.2 V and CL = 750 pF. The oscillation frequencies are 40.2, 30.5, 26.1 and 16.8 kHz for RL = 50, 80, 100 and 150 kΩ, respectively. The oscillation frequency decreases as RL increases, as shown in Fig. 3(d). The test results indicate that this Ag NDs TS oscillation neuron exhibits self-oscillation behavior, and the output oscillation frequency can be used to sense the weight of the RRAM synapse accurately when the neuron is connected to the crossbar array.

Figure  3.  (Color online) (a) Oscillation waveforms of the oscillation neuron with different Vin when RL = 50 kΩ, CL = 750 pF. (b) The oscillation frequency as a function of Vin. (c) Oscillation waveforms of the oscillation neuron with different RL when Vin = 1.2 V, CL = 750 pF. (d) The oscillation frequency as a function of RL.
DownLoad: Full-Size Img PowerPoint

Based on experimentally derived device data, a model of the RRAM crossbar array is developed to evaluate synaptic weights of different array sizes. Then SIPCE simulation is used to investigate the relationship between the oscillation frequency and the RRAM crossbar array size. In order to simplify the simulation process, the resistance RRAM device is fixed at an intermediate resistance state (RL = 500 kΩ), and Vin is applied to all the word lines (WLs) in parallel. The results are shown in Fig. 4. If the on/off ratio of the TS device is less than 100, only a limited range of the weighted sum could meet the criterion for oscillation (Roff > RL > Ron). It means that the oscillation neuron can only be used for small crossbar arrays (< 16 × 16). With the increase of on/off ratio, the oscillation neuron can work with a wider range of load resistance. Therefore, the weighted sum in a larger RRAM crossbar array can be successfully identified by distinguishable oscillation frequency. As in the case of our present Ag NDs TS device with on/off ratio > 108, it can work with a large RRAM crossbar array of size > 128 × 128.

Figure  4.  (Color online) The oscillation frequency as a function of the RRAM crossbar array size under different on/off ratios of the TS device.
DownLoad: Full-Size Img PowerPoint

In order to investigate the impact of TS device variation on the oscillation neuron, the oscillation frequency distribution with different CV under the same experimental conditions is shown in Fig. 5(a). The uniformity of the TS device deteriorates with the increase of CV, which leads to a large variation of the oscillation frequency under the same load condition. Fig. 5(b) shows the oscillation frequency distribution of different RL with different TS devices. Compared with the Ag thin film device (CV ~ 30%)[23], the output frequency distribution of the Ag NDs TS neuron (CV ~ 7%) is more concentrated. Therefore, the oscillation neuron based on the Ag NDs TS device can achieve higher accuracy for neuromorphic computing.

Figure  5.  (Color online) (a) The oscillation frequency distribution under different CV. (b) The oscillation frequency distribution of different RL when CV = 7% (top panel) and CV = 30% (bottom panel).
DownLoad: Full-Size Img PowerPoint

In order to further analyze the impact of the TS device’s uniformity on the artificial neural network, a multi-layer perceptron (MLP) of 784 × 200 × 10 is simulated to classify the handwritten digits in the Modified National Institute of Standards and Technology (MNIST) dataset, as shown in Fig. 6(a)[25]. The simulation results displayed in Fig. 6(b) indicate that the increase in CV leads to a dramatic degradation of the recognition accuracy, especially when CV > 15%. Therefore, the oscillation neuron based on the high uniformity Ag NDs TS device developed in this work is beneficial to reduce the network accuracy loss.

Figure  6.  (Color online) (a) The structure of MLP neural network. (b) Simulation results of the MNIST recognition accuracy loss as a function of the variability of the TS device.
DownLoad: Full-Size Img PowerPoint

In conclusion, we have demonstrated a reliable oscillation neuron using the low-variability Ag NDs TS device for high-performance neuromorphic computing. This neuron exhibits self-oscillation behavior at low applied voltages down to 1 V. A systematic study on the oscillation characteristics reveals that the oscillation frequency increases with the applied voltage and also the synaptic conductance connected as the load resistor. The high uniformity and large on/off ratio of the Ag NDs TS device enable the oscillation neuron to reduce the neural network accuracy loss (< 1%) and make it applicable to a large-scale RRAM crossbar array (> 128 × 128). The developed oscillation neuron hence has great potential for future neuromorphic system applications.

This work was supported in part by China Key Research and Development Program (2016YFA0201800), and the National Natural Science Foundation of China (91964104, 61974081).



[1]
Indiveri G, Linares-Barranco B, Legenstein R, et al. Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology, 2013, 24, 384010 doi: 10.1088/0957-4484/24/38/384010
[2]
Ambrogio S, Balatti S, Milo V, et al. Neuromorphic learning and recognition with one-transistor-one-resistor synapses and bistable metal oxide RRAM. IEEE Trans Electron Devices, 2016, 63, 1508 doi: 10.1109/TED.2016.2526647
[3]
Burr G W, Shelby R M, Sebastian A, et al. Neuromorphic computing using non-volatile memory. Adv Phys X, 2017, 2, 89 doi: 10.1080/23746149.2016.1259585
[4]
Zidan M A, Strachan J P, Lu W D. The future of electronics based on memristive systems. Nat Electron, 2018, 1, 22 doi: 10.1038/s41928-017-0006-8
[5]
Merrikh B F, Prezioso M, Chakrabarti B, et al. Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits. Nat Commun, 2017, 9, 2331 doi: 10.1038/s41467-018-04482-4
[6]
Yao P, Wu H Q, Gao B, et al. Fully hardware-implemented memristor convolutional neural network. Nature, 2020, 577, 641 doi: 10.1038/s41586-020-1942-4
[7]
Choi S, Yang J, Wang G. Emerging memristive artificial synapses and neurons for energy-efficient neuromorphic computing. Adv Mater, 2020, 32, 2004659 doi: 10.1002/adma.202004659
[8]
Zhu J D, Zhang T, Yang Y C, et al. A comprehensive review on emerging artificial neuromorphic devices. Appl Phys Rev, 2020, 7, 011312 doi: 10.1063/1.5118217
[9]
Kadetotad D, Xu Z H, Mohanty A, et al. Parallel architecture with resistive crosspoint array for dictionary learning acceleration. IEEE J Emerg Sel Top Circuits Syst, 2015, 5, 194 doi: 10.1109/JETCAS.2015.2426495
[10]
Hua Q L, Wu H Q, Gao B, et al. Low-voltage oscillatory neurons for memristor-based neuromorphic systems. Glob Challenges, 2019, 3, 1900015 doi: 10.1002/gch2.201900015
[11]
Dang B J, Liu K Q, Zhu J D, et al. Stochastic neuron based on IGZO Schottky diodes for neuromorphic computing. APL Mater, 2019, 7, 071114 doi: 10.1063/1.5109090
[12]
Li S, Liu X J, Nandi S K, et al. High-endurance MHz electrical self-oscillation in Ti/NbOx bilayer structures. Appl Phys Lett, 2015, 106, 212902 doi: 10.1063/1.4921745
[13]
Gao L G, Chen P Y, Yu S M. NbOx based oscillation neuron for neuromorphic computing. Appl Phys Lett, 2017, 111, 103503 doi: 10.1063/1.4991917
[14]
Duan Q X, Jing Z K, Yang K, et al. Oscillation neuron based on threshold switching characteristics of niobium oxide films. 2019 IEEE International Workshop on Future Computing, 2019, 1
[15]
Woo J, Wang P N, Yu S M. Integrated crossbar array with resistive synapses and oscillation neurons. IEEE Electron Device Lett, 2019, 40, 1313 doi: 10.1109/LED.2019.2921656
[16]
Wang P N, Khan A I, Yu S M. Cryogenic behavior of NbO2 based threshold switching devices as oscillation neurons. Appl Phys Lett, 2020, 116, 162108 doi: 10.1063/5.0006467
[17]
Luo Q, Xu X, Lv H, et al. Cu BEOL compatible selector with high selectivity (> 107), extremely low off-current (pA) and high endurance (> 1010). 2015 IEEE International Electron Devices Meeting (IEDM), 2015, 10.4.1
[18]
Yoo J, Woo J, Song J, et al. Threshold switching behavior of Ag-Si based selector device and hydrogen doping effect on its characteristics. AIP Adv, 2015, 5, 127221 doi: 10.1063/1.4938548
[19]
Du G, Wang C, Li H X, et al. Bidirectional threshold switching characteristics in Ag/ZrO2/Pt electrochemical metallization cells. AIP Adv, 2016, 6, 085316 doi: 10.1063/1.4961709
[20]
Wang Z R, Rao M Y, Midya R, et al. Threshold switching: Threshold switching of Ag or Cu in dielectrics: Materials, mechanism, and applications. Adv Funct Mater, 2018, 28, 1870036 doi: 10.1002/adfm.201870036
[21]
Yoo J, Park J, Song J, et al. Field-induced nucleation in threshold switching characteristics of electrochemical metallization devices. Appl Phys Lett, 2017, 111, 063109 doi: 10.1063/1.4985165
[22]
Wang W, Wang M, Ambrosi E, et al. Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices. Nat Commun, 2019, 10, 81 doi: https://doi.org/10.1038/s41467-018-07979-0
[23]
Hua Q L, Wu H Q, Gao B, et al. Threshold switching selectors: A threshold switching selector based on highly ordered Ag nanodots for X-point memory applications. Adv Sci, 2019, 6, 1970058 doi: 10.1002/advs.201970058
[24]
Li Y J, Tang J S, Gao B, et al. High-uniformity threshold switching HfO2 -based selectors with patterned Ag nanodots. Adv Sci, 2020, 7, 2002251 doi: 10.1002/advs.202002251
[25]
Xi Y, Gao B, Tang J S, et al. In-memory learning with analog resistive switching memory: A review and perspective. Proc IEEE, 2021, 109, 14 doi: 10.1109/JPROC.2020.3004543
Fig. 1.  (Color online) (a) Schematic diagram of a typical artificial neural network. (b) Circuit implementation of the oscillation neuron with a TS device.

DownLoad: Full-Size Img PowerPoint
Fig. 2.  (Color online) (a) TEM image of the Ag NDs TS device. (b) Schematic illustration of the threshold switching process in the device. (c) Typical current–voltage (I–V) curves for the Ag NDs TS device. (d) Cumulative probability of Vth and Vhold distributions for the Ag NDs TS device. (e) Endurance test of the Ag NDs TS device with over 108 cycles. (f) Measured oscillation waveform of the oscillation neuron.

DownLoad: Full-Size Img PowerPoint
Fig. 3.  (Color online) (a) Oscillation waveforms of the oscillation neuron with different Vin when RL = 50 kΩ, CL = 750 pF. (b) The oscillation frequency as a function of Vin. (c) Oscillation waveforms of the oscillation neuron with different RL when Vin = 1.2 V, CL = 750 pF. (d) The oscillation frequency as a function of RL.

DownLoad: Full-Size Img PowerPoint
Fig. 4.  (Color online) The oscillation frequency as a function of the RRAM crossbar array size under different on/off ratios of the TS device.

DownLoad: Full-Size Img PowerPoint
Fig. 5.  (Color online) (a) The oscillation frequency distribution under different CV. (b) The oscillation frequency distribution of different RL when CV = 7% (top panel) and CV = 30% (bottom panel).

DownLoad: Full-Size Img PowerPoint
Fig. 6.  (Color online) (a) The structure of MLP neural network. (b) Simulation results of the MNIST recognition accuracy loss as a function of the variability of the TS device.

DownLoad: Full-Size Img PowerPoint
[1]
Indiveri G, Linares-Barranco B, Legenstein R, et al. Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology, 2013, 24, 384010 doi: 10.1088/0957-4484/24/38/384010
[2]
Ambrogio S, Balatti S, Milo V, et al. Neuromorphic learning and recognition with one-transistor-one-resistor synapses and bistable metal oxide RRAM. IEEE Trans Electron Devices, 2016, 63, 1508 doi: 10.1109/TED.2016.2526647
[3]
Burr G W, Shelby R M, Sebastian A, et al. Neuromorphic computing using non-volatile memory. Adv Phys X, 2017, 2, 89 doi: 10.1080/23746149.2016.1259585
[4]
Zidan M A, Strachan J P, Lu W D. The future of electronics based on memristive systems. Nat Electron, 2018, 1, 22 doi: 10.1038/s41928-017-0006-8
[5]
Merrikh B F, Prezioso M, Chakrabarti B, et al. Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits. Nat Commun, 2017, 9, 2331 doi: 10.1038/s41467-018-04482-4
[6]
Yao P, Wu H Q, Gao B, et al. Fully hardware-implemented memristor convolutional neural network. Nature, 2020, 577, 641 doi: 10.1038/s41586-020-1942-4
[7]
Choi S, Yang J, Wang G. Emerging memristive artificial synapses and neurons for energy-efficient neuromorphic computing. Adv Mater, 2020, 32, 2004659 doi: 10.1002/adma.202004659
[8]
Zhu J D, Zhang T, Yang Y C, et al. A comprehensive review on emerging artificial neuromorphic devices. Appl Phys Rev, 2020, 7, 011312 doi: 10.1063/1.5118217
[9]
Kadetotad D, Xu Z H, Mohanty A, et al. Parallel architecture with resistive crosspoint array for dictionary learning acceleration. IEEE J Emerg Sel Top Circuits Syst, 2015, 5, 194 doi: 10.1109/JETCAS.2015.2426495
[10]
Hua Q L, Wu H Q, Gao B, et al. Low-voltage oscillatory neurons for memristor-based neuromorphic systems. Glob Challenges, 2019, 3, 1900015 doi: 10.1002/gch2.201900015
[11]
Dang B J, Liu K Q, Zhu J D, et al. Stochastic neuron based on IGZO Schottky diodes for neuromorphic computing. APL Mater, 2019, 7, 071114 doi: 10.1063/1.5109090
[12]
Li S, Liu X J, Nandi S K, et al. High-endurance MHz electrical self-oscillation in Ti/NbOx bilayer structures. Appl Phys Lett, 2015, 106, 212902 doi: 10.1063/1.4921745
[13]
Gao L G, Chen P Y, Yu S M. NbOx based oscillation neuron for neuromorphic computing. Appl Phys Lett, 2017, 111, 103503 doi: 10.1063/1.4991917
[14]
Duan Q X, Jing Z K, Yang K, et al. Oscillation neuron based on threshold switching characteristics of niobium oxide films. 2019 IEEE International Workshop on Future Computing, 2019, 1
[15]
Woo J, Wang P N, Yu S M. Integrated crossbar array with resistive synapses and oscillation neurons. IEEE Electron Device Lett, 2019, 40, 1313 doi: 10.1109/LED.2019.2921656
[16]
Wang P N, Khan A I, Yu S M. Cryogenic behavior of NbO2 based threshold switching devices as oscillation neurons. Appl Phys Lett, 2020, 116, 162108 doi: 10.1063/5.0006467
[17]
Luo Q, Xu X, Lv H, et al. Cu BEOL compatible selector with high selectivity (> 107), extremely low off-current (pA) and high endurance (> 1010). 2015 IEEE International Electron Devices Meeting (IEDM), 2015, 10.4.1
[18]
Yoo J, Woo J, Song J, et al. Threshold switching behavior of Ag-Si based selector device and hydrogen doping effect on its characteristics. AIP Adv, 2015, 5, 127221 doi: 10.1063/1.4938548
[19]
Du G, Wang C, Li H X, et al. Bidirectional threshold switching characteristics in Ag/ZrO2/Pt electrochemical metallization cells. AIP Adv, 2016, 6, 085316 doi: 10.1063/1.4961709
[20]
Wang Z R, Rao M Y, Midya R, et al. Threshold switching: Threshold switching of Ag or Cu in dielectrics: Materials, mechanism, and applications. Adv Funct Mater, 2018, 28, 1870036 doi: 10.1002/adfm.201870036
[21]
Yoo J, Park J, Song J, et al. Field-induced nucleation in threshold switching characteristics of electrochemical metallization devices. Appl Phys Lett, 2017, 111, 063109 doi: 10.1063/1.4985165
[22]
Wang W, Wang M, Ambrosi E, et al. Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices. Nat Commun, 2019, 10, 81 doi: https://doi.org/10.1038/s41467-018-07979-0
[23]
Hua Q L, Wu H Q, Gao B, et al. Threshold switching selectors: A threshold switching selector based on highly ordered Ag nanodots for X-point memory applications. Adv Sci, 2019, 6, 1970058 doi: 10.1002/advs.201970058
[24]
Li Y J, Tang J S, Gao B, et al. High-uniformity threshold switching HfO2 -based selectors with patterned Ag nanodots. Adv Sci, 2020, 7, 2002251 doi: 10.1002/advs.202002251
[25]
Xi Y, Gao B, Tang J S, et al. In-memory learning with analog resistive switching memory: A review and perspective. Proc IEEE, 2021, 109, 14 doi: 10.1109/JPROC.2020.3004543
1

Electrolyte-gated optoelectronic transistors for neuromorphic applications

Jinming Bi, Yanran Li, Rong Lu, Honglin Song, Jie Jiang, et al.

Journal of Semiconductors, 2025, 46(2): 021401. doi: 10.1088/1674-4926/24090042
2

Revolutionizing neuromorphic computing with memristor-based artificial neurons

Yanning Chen, Guobin Zhang, Fang Liu, Bo Wu, Yongfeng Deng, et al.

Journal of Semiconductors. doi: 10.1088/1674-4926/24110006
3

Synaptic devices based on silicon carbide for neuromorphic computing

Boyu Ye, Xiao Liu, Chao Wu, Wensheng Yan, Xiaodong Pi, et al.

Journal of Semiconductors, 2025, 46(2): 021403. doi: 10.1088/1674-4926/24100020
4

Ion-modulation optoelectronic neuromorphic devices: mechanisms, characteristics, and applications

Xiaohan Meng, Runsheng Gao, Xiaojian Zhu, Run-Wei Li

Journal of Semiconductors, 2025, 46(2): 021402. doi: 10.1088/1674-4926/24100025
5

Electropolymerized dopamine-based memristors using threshold switching behaviors for artificial current-activated spiking neurons

Bowen Zhong, Xiaokun Qin, Zhexin Li, Yiqiang Zheng, Lingchen Liu, et al.

Journal of Semiconductors, 2025, 46(2): 022402. doi: 10.1088/1674-4926/24070007
6

InGaZnO-based photoelectric synaptic devices for neuromorphic computing

Jieru Song, Jialin Meng, Tianyu Wang, Changjin Wan, Hao Zhu, et al.

Journal of Semiconductors, 2024, 45(9): 092402. doi: 10.1088/1674-4926/24040038
7

Complementary memtransistors for neuromorphic computing: How, what and why

Qi Chen, Yue Zhou, Weiwei Xiong, Zirui Chen, Yasai Wang, et al.

Journal of Semiconductors, 2024, 45(6): 061701. doi: 10.1088/1674-4926/23120051
8

Volatile threshold switching memristor: An emerging enabler in the AIoT era

Wenbin Zuo, Qihang Zhu, Yuyang Fu, Yu Zhang, Tianqing Wan, et al.

Journal of Semiconductors, 2023, 44(5): 053102. doi: 10.1088/1674-4926/44/5/053102
9

Preface to the Special Issue on Beyond Moore: Resistive Switching Devices for Emerging Memory and Neuromorphic Computing

Yue Hao, Huaqiang Wu, Yuchao Yang, Qi Liu, Xiao Gong, et al.

Journal of Semiconductors, 2021, 42(1): 010101. doi: 10.1088/1674-4926/42/1/010101
10

Towards engineering in memristors for emerging memory and neuromorphic computing: A review

Andrey S. Sokolov, Haider Abbas, Yawar Abbas, Changhwan Choi

Journal of Semiconductors, 2021, 42(1): 013101. doi: 10.1088/1674-4926/42/1/013101
11

Engineering in-plane silicon nanowire springs for highly stretchable electronics

Zhaoguo Xue, Taige Dong, Zhimin Zhu, Yaolong Zhao, Ying Sun, et al.

Journal of Semiconductors, 2018, 39(1): 011001. doi: 10.1088/1674-4926/39/1/011001
12

Study of the effect of switching speed of the a-SiC/c-Si (p)-based, thyristor-like, ultra-high-speed switches, using two-dimensional simulation techniques

Evangelos I. Dimitriadis, Nikolaos Georgoulas

Journal of Semiconductors, 2017, 38(5): 054001. doi: 10.1088/1674-4926/38/5/054001
13

Comparison between Pt/TiO2/Pt and Pt/TaOX/TaOY/Pt based bipolar resistive switching devices

Patrick W. C. Ho, Firas Odai Hatem, Haider Abbas F. Almurib, T. Nandha Kumar

Journal of Semiconductors, 2016, 37(6): 064001. doi: 10.1088/1674-4926/37/6/064001
14

Low-power switched-capacitor delta-sigma modulator for EEG recording applications

Chen Jin, Zhang Xu, Chen Hongda

Journal of Semiconductors, 2010, 31(7): 075009. doi: 10.1088/1674-4926/31/7/075009
15

A dual VCDL DLL based gate driver for zero-voltage-switching DC-DC converter

Tian Xin, Liu Xiangxin, Li Wenhong

Journal of Semiconductors, 2010, 31(7): 075012. doi: 10.1088/1674-4926/31/7/075012
16

Fabrication and Evaluation of Bragg Gratings on Optimally Designed Silicon-on-Insulator Rib Waveguides Using Electron-Beam Lithography

Wu Zhigang, Zhang Weigang, Wang Zhi, Kai Guiyun, Yuan Shuzhong, et al.

Chinese Journal of Semiconductors , 2006, 27(8): 1347-1350.

17

Simulation of a Double-Gate Dynamic Threshold Voltage Fully Depleted Silicon-on-Insulator nMOSFET

Bi Jinshun, Wu Junfeng, Hai Chaohe

Chinese Journal of Semiconductors , 2006, 27(1): 35-40.

18

Fabrication and Numerical Simulation of a Micromachined Contact Cantilever RF-MEMS Switch

Sun Jianhai, Cui Dafu, Xiao Jiang

Chinese Journal of Semiconductors , 2006, 27(2): 309-312.

19

Peculiar Photoconduction in Semi-Insulating GaAs Photoconductive Switch Triggered by 1064nm Laser Pulse

Shi Wei, Dai Huiying, and Zhang Xianbin

Chinese Journal of Semiconductors , 2005, 26(3): 460-464.

20

Ultra-Wideband Electromagnetic Radiation from GaAs Photoconductive Switches

Shi Wei，Jia Wanli, and Ji Weili

Chinese Journal of Semiconductors , 2005, 26(1): 11-15.

1. Ostrovskii, V., Karimov, T., Rybin, V. et al. Bio-inspired neuron based on threshold selector and tunnel diode capable of excitability modulation. Neurocomputing, 2025. doi:10.1016/j.neucom.2025.129454
2. Tian, Q., Xun, K., Li, Z. et al. Optoelectronic memristor based on a-C:Te film for muti-mode reservoir computing. Journal of Semiconductors, 2025, 46(2): 022407. doi:10.1088/1674-4926/24100008
3. Wang, L., Zhang, L., Hua, S. et al. Leaky integrate-and-fire and oscillation neurons based on ZnO diffusive memristors for spiking neural networks | [基于ZnO扩散型忆阻器的漏电整合发放神经元和振荡神经元用于脉冲神经网络]. Science China Materials, 2025. doi:10.1007/s40843-024-3236-6
4. Chen, J., Yin, L., Wang, Y. et al. Artificial afferent neurons based on the metal-insulator transition of VO2. Science China Information Sciences, 2024, 67(11): 212401. doi:10.1007/s11432-023-3959-6
5. Chen, S., Zhang, T., Tappertzhofen, S. et al. Electrochemical-Memristor-Based Artificial Neurons and Synapses—Fundamentals, Applications, and Challenges. Advanced Materials, 2023, 35(37): 2301924. doi:10.1002/adma.202301924
6. Li, X., Zhong, Y., Chen, H. et al. A Memristors-Based Dendritic Neuron for High-Efficiency Spatial-Temporal Information Processing. Advanced Materials, 2023, 35(37): 2203684. doi:10.1002/adma.202203684
7. Kim, H.W., Jeon, S., Kang, H. et al. Understanding Rhythmic Synchronization of Oscillatory Neural Networks Based on NbOx Artificial Neurons for Edge Detection. IEEE Transactions on Electron Devices, 2023, 70(6): 3031-3036. doi:10.1109/TED.2023.3263818
8. Wen, J., Zhu, Z.-Y., Guo, X. Artificial visual neuron based on threshold switching memristors. Neuromorphic Computing and Engineering, 2023, 3(1): 014015. doi:10.1088/2634-4386/acc050
9. Chen, J., Li, L., Ran, W. et al. An intelligent MXene/MoS2 acoustic sensor with high accuracy for mechano-acoustic recognition. Nano Research, 2023, 16(2): 3180-3187. doi:10.1007/s12274-022-4973-3
10. Xi, Y., Li, X., Chen, J. et al. Reliability of Memristive Devices for High-Performance Neuromorphic Computing: (Invited Paper). IEEE International Reliability Physics Symposium Proceedings, 2023. doi:10.1109/IRPS48203.2023.10118214
11. Chen, J., Zheng, X., Tang, J. et al. Microscopic Modeling and Optimization of NbOxMott Memristor for Artificial Neuron Applications. IEEE Transactions on Electron Devices, 2022, 69(12): 6686-6692. doi:10.1109/TED.2022.3212325
12. Chen, P., Zhang, X., Wu, Z. et al. High-Yield and Uniform NbOx-Based Threshold Switching Devices for Neuron Applications. IEEE Transactions on Electron Devices, 2022, 69(5): 2391-2397. doi:10.1109/TED.2022.3161614
13. Lu, Y., Zhai, L., Gao, B. et al. Device Modeling and Simulation of Ferroelectric Tunnel Junction for Computing-in-Memory Application. 2022. doi:10.1109/CSTIC55103.2022.9856788

    • Search

    Advanced Search >>

    GET CITATION

    Yujia Li, Jianshi Tang, Bin Gao, Xinyi Li, Yue Xi, Wanrong Zhang, He Qian, Huaqiang Wu. Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing[J]. Journal of Semiconductors, 2021, 42(6): 064101. doi: 10.1088/1674-4926/42/6/064101
    Y J Li, J S Tang, B Gao, X Y Li, Y Xi, W R Zhang, H Qian, H Q Wu, Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing[J]. J. Semicond., 2021, 42(6): 064101. doi: 10.1088/1674-4926/42/6/064101.
    shu

    Export: BibTex EndNote

    Article Metrics

    Article has an altmetric score of 1
    Article has an altmetric score of 1

    Article views: 3995 Times PDF downloads: 141 Times Cited by: 13 Times

    History

    Received: 06 January 2021 Revised: 08 February 2021 Online: Accepted Manuscript: 17 March 2021Uncorrected proof: 27 March 2021Published: 01 June 2021

    Catalog

      Email This Article

      User name:
      Email:*请输入正确邮箱
      Code:*验证码错误
      Send
      Article Navigation >  Journal of Semiconductors  > 2021  >  42(6): 064101
      Yujia Li, Jianshi Tang, Bin Gao, Xinyi Li, Yue Xi, Wanrong Zhang, He Qian, Huaqiang Wu. Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing[J]. Journal of Semiconductors, 2021, 42(6): 064101. doi: 10.1088/1674-4926/42/6/064101 ****Y J Li, J S Tang, B Gao, X Y Li, Y Xi, W R Zhang, H Qian, H Q Wu, Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing[J]. J. Semicond., 2021, 42(6): 064101. doi: 10.1088/1674-4926/42/6/064101.
      Citation:
      Yujia Li, Jianshi Tang, Bin Gao, Xinyi Li, Yue Xi, Wanrong Zhang, He Qian, Huaqiang Wu. Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing[J]. Journal of Semiconductors, 2021, 42(6): 064101. doi: 10.1088/1674-4926/42/6/064101 ****
      Y J Li, J S Tang, B Gao, X Y Li, Y Xi, W R Zhang, H Qian, H Q Wu, Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing[J]. J. Semicond., 2021, 42(6): 064101. doi: 10.1088/1674-4926/42/6/064101.
      shu

      Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing

      DOI: 10.1088/1674-4926/42/6/064101
      • 1.

        Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

      • 2.

        Institute of Microelectronics, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China

      • 3.

        Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing 100084, China

      More Information
      • Yujia Li：is currently a joint PhD. student of Faculty of Information Technology, Beijing University of Technology and Institute of Microelectronics, Tsinghua University from 2016. Her current research interests include design and optimization of resistive switching memory and selector devices as well as their applications in neuromorphic computing
      • Jianshi Tang：(Senior Member, IEEE) received the B.S. degree in microelectronics and nano-electronics from Tsinghua University, Beijing, China, in 2008, and the Ph.D. degree in electrical engineering from the University of California at Los Angeles, Los Angeles, CA, USA, in 2014. From 2015 to 2019, he worked at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. He is currently an Assistant Professor with the Institute of Microelectronics, Tsinghua University. He has published over 100 journal articles and conference proceedings, and filed over 60 patents, 20 of which have been granted. His current research interests include emerging memory and neuromorphic computing and carbon nanotube electronics. Prof. Tang has received many awards, including the National Young Thousand Talents Plan, the NT18 Best Young Scientist Award, the IEEE Best Lightning Talk, and the IBM Invention Achievement Awards
      • Bin Gao：(Senior Member, IEEE) received the B.S. degree in physics and the Ph.D. degree in microelectronics from Peking University, Beijing, China, in 2008 and 2013, respectively. He is currently an Associate Professor of microelectronics with Tsinghua University, Beijing. His current research interests include emerging memory devices and neuromorphic computing
      • Xinyi Li：received the Ph.D. degree in Microelectronics and Solid State Electronics from TianJin University, TianJin, China, in 2010. She is currently working with the Institute of Microelectronics, Tsinghua University. Her current research interests include neuromorphic devices and their application in neuromorphic computing
      • Yue Xi：(Student Member, IEEE) received the bachelor’s degree in microelectronics from Xi’an Jiaotong University, Xi’an, China, in 2017. He is currently pursuing the Ph.D. degree in microelectronics with Tsinghua University, Beijing, China. His current research interests include ana-log resistive switching memory devices and memristor-based neuromorphic computing
      • Wanrong Zhang：received the B.S. and M.S. degrees in microelectronics and solid-state electronics from Lanzhou University, Lanzhou, China, in 1987 and 1990, respectively, and the Ph.D. degree in microelectronics and solid-state electronics from Xi’an Jiaotong University, Xi’an, China, in 1996. He is currently a Professor and a Ph.D. Supervisor with the Faculty of Information Technology, Beijing University of Technology, Beijing, China. His current research interests include RF/microwave/ millimeter-wave devices and circuits, mixed-signal circuits, cryogenic electronics, device-to-circuit interactions, noise and linearity, reliability physics, device-level simulation, and compact circuit modeling
      • He Qian：(Member, IEEE) received the Ph.D. degree in microelectronics from Xi’an Jiao-tong University, Xi’an, China, in 1990. From 1990 to 2006, he worked with the Institute of Microelectronics, Chinese Academy of Sciences (IMECAS), Beijing, China, where he became a Professor in 1996 and the Director in 2001. From 2006 to 2008, he worked for the Samsung Semiconductor China Research and Development Center (SSCR), Nanjing, China, as the Director. In 2009, he joined the Institute of Micro-electronics, Tsinghua University (IMTU), Beijing, as a Professor. His current research interests include resistive random access memory (RRAM), 3-D NAND, and neuromorphic computing based on RRAM array
      • Huaqiang Wu：(Senior Member, IEEE) received the double B.S. degrees in material science and engineering and enterprise management from Tsinghua University, Beijing, China, in 2000, and the Ph.D. degree in electrical engineering from Cornell University, Ithaca, NY, USA, in 2005. From 2006 to 2008, he was a Senior Engineer with Spansion LLC, Sunnyvale, CA, USA. He joined the Institute of Microelectronics, Tsinghua University, in 2009, where he is currently a Professor and the Director of the Institute of Microelectronics. He also serves as the Deputy Director of the Beijing Innovation Center for Future Chip (ICFC). He has published more than 100 technical articles and owns more than 60 patents. His research interests include emerging memories and neuromorphic computing
      • Corresponding author: jtang@tsinghua.edu.cn; wuhq@tsinghua.edu.cn
      • Received Date: 2021-01-06
      • Revised Date: 2021-02-08
      • Published Date: 2021-06-10

      Catalog

        Journal of Semiconductors © 2017 All Rights Reserved   京ICP备05085259号-2

        /

        DownLoad:  Full-Size Img  PowerPoint
        Return
        Return