Mobile QR Code QR CODE

The Journal of Semiconductor Technology and Science (JSTS) is an international, peer-reviewed, and open-access journal that is published bimonthly.
- Scope: semiconductor processes, devices, circuits, and MEMS.
- Editor-in-Chief: Prof. Woo Young Choi (ECE, Seoul National University)
- Indexed within Science Citation Index Expanded (SCIE), SCOPUS, Korea Citation Index (KCI), and other databases.

Journal Search

  1. Home
  2. Archive
  3. 2025-04 (Vol.25 No.02)
  4. 10.5573/JSTS.2025.25.2.109
XML PDF INFO REF

References

1 
Strukov, Dmitri, et al., ``Building brain-inspired computing,'' Nature Communications, vol. 10, pp. 4838-2019, 2019.DOI
2 
M. Tsodyks and C. Gilbert. ``Neural networks andperceptual learning,'' Nature, vol. 431, no. 7010, pp. 775-781, 2004.DOI
3 
C. Mead, ``Neuromorphic electronic systems,'' Proceedings of the IEEE, vol. 78, no. 10, pp. 1629–1636, 1990.DOI
4 
H. Yu, et al., ``Evolution of bio‐inspired artificial synapses: Materials, structures, and mechanisms,'' Small, vol. 17, no. 9, 2000041, 2021.DOI
5 
V. M. Ho,, J.-A. Lee, and K. C. Martin. ``The cell biology of synaptic plasticity,'' Science, vol. 334, no. 6056, pp. 623-628, 2011.DOI
6 
H.-J. Park and K. Friston. ``Structural and functional brain networks: From connections to cognition,'' Science, vol. 342, no. 6158, 1238411, 2013.DOI
7 
I. B. Levitan and L. K. Kaczmarek, The Neuron: Cell and Molecular Biology, Oxford University Press, USA, 2015.URL
8 
M. di Filippo, et al. ``Short-term and long-term plasticity at corticostriatal synapses: implications for learning and memory,'' Behavioural Brain Research, vol. 199, no. 1, pp. 108-118, 2009.DOI
9 
H. R. Monday, T. J. Younts, and P. E. Castillo. ``Long-term plasticity of neurotransmitter release: Emerging mechanisms and contributions to brain function and disease,'' Annual Review of Neuroscience, vol. 41, no. 1, pp. 299-322, 2018.DOI
10 
J.-Q. Yang, et al., ``Neuromorphic engineering: From biological to spike‐based hardware nervous systems,'' Advanced Materials, vol. 32, no. 52, 2003610, 2020.DOI
11 
S. B. Furber, F. Galluppi, S. Temple, and L. A. Plana, ``The SpiNNaker project,'' Proceedings of the IEEE, vol. 102, no. 5, pp. 652–665, May 2014.DOI
12 
I. Sourikopoulos, et al., ``A 4-FJ/Spike artificial neuron in 65 nm CMOS technology,'' Frontiers in Neuroscience, vol. 11, 123, 2017.DOI
13 
W. Maass, ``Networks of spiking neurons: the third generation of neural network models,'' Neural Networks, vol. 10, no. 9, pp. 1659-1671, 1997.DOI
14 
H. S. Seung, ``Learning in spiking neural networks by reinforcement of stochastic synaptic transmission,'' Neuron, vol. 40, no. 6, pp. 1063-1073, 2003.DOI
15 
D. Kwon, S. Y. Woo, and J .H. Lee, ``Review of analog neuron devices for hardware-based spiking neural networks,'' Journal of Semiconductor Technology and Science, vol. 22, no. 2, pp. 115-131, 2022.DOI
16 
J. H. B. Wijekoon and P. Dudek. ``A CMOS circuit implementation of a spiking neuron with bursting and adaptation on a biological timescale,'' Proc. of IEEE Biomedical Circuits and Systems Conference, IEEE, 2009.DOI
17 
H. Tanaka, T. Morie, and K. Aihara. ``An analog CMOS circuit for spiking neuron models,'' International Congress Series, Elsevier, vol. 1291, 2006.DOI
18 
B. Joo, J.-W. Han, and B.-S. Kong, ``Energy-and area-efficient CMOS synapse and neuron for spiking neural networks with STDP learning,'' IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 69, no. 9, pp. 3632-3642, 2022.DOI
19 
C.-S. Poon and K. Zhou. ``Neuromorphic silicon neurons and large-scale neural networks: challenges and opportunities,'' Frontiers in Neuroscience, vol. 5, 108, 2011.DOI
20 
M.-W. Kwon, et al., ``Integrate-and-fire neuron circuit using positive feedback field effect transistor for low power operation,'' Journal of Applied Physics, vol. 124, no. 15, 2018.DOI
21 
J. Lee, M. Cha, and M,-W, Kwon. ``Capacitor-less low-power neuron circuit with multi-gate feedback field effect transistor,'' Applied Sciences, vol. 13, no. 4, 2628, 2023.DOI
22 
N. Lynch, C. Musco, and M. Parter, ``Winner-take-all computation in spiking neural networks,'' arXiv preprint arXiv:1904.12591, 2019.DOI
23 
Y. Fang, M. A. Cohen, and T. G. Kincaid. ``Dynamics of a winner-take-all neural network,'' Neural Networks, vol. 9, no. 7, pp. 1141-1154, 1996.DOI
24 
T. Fukai and S. Tanaka. ``A simple neural network exhibiting selective activation of neuronal ensembles: from winner-take-all to winners-share-all,'' Neural Computation, vol. 9, no. 1, pp. 77-97, 1997.DOI
25 
Z.-H. Mao and S. G. Massaquoi. ``Dynamics of winner-take-all competition in recurrent neural networks with lateral inhibition,'' IEEE Transactions on Neural Networks, vol. 18, no. 1, pp. 55-69, 2007.DOI
26 
P. U. Diehl and M, Cook. ``Unsupervised learning of digit recognition using spike-timing-dependent plasticity,'' Frontiers in Computational Neuroscience, vol. 9, 99, 2015.DOI