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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.

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  3. 2025-10 (Vol.25 No.05)
  4. 10.5573/JSTS.2025.25.5.469
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References

1 
N. Horta, ``Analogue and mixed-signal systems topologies exploration using symbolic methods,'' Analog Integrated Circuits and Signal Processing, vol. 31, pp. 161-176, 2002.DOI
2 
N. Jangkrajarng, S. Bhattacharya, R. Hartono, and C.-J. R. Shi, ``Iprail—intellectual property reuse-based analog ic layout automation,'' Integration, vol. 36, no. 4, pp. 237-262, 2003.DOI
3 
H. Wang, J. Yang, H.-S. Lee, and S. Han, ``Learning to design circuits,'' arXiv preprint arXiv:1812.02734, 2018.DOI
4 
N. K. Somayaji, H. Hu, and P. Li, ``Prioritized reinforcement learning for analog circuit optimization with design knowledge,'' Proc. of 58th ACM/IEEE Design Automation Conference (DAC), IEEE, pp. 1231-1236, 2021.DOI
5 
Y. Choi, S. Park, M. Choi, K. Lee, and S. Kang, ``Ma-opt: Reinforcement learning-based analog circuit optimization using multi-actors,'' IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 71, no. 5, pp. 2045-2056, 2024.DOI
6 
H. Wang, K. Wang, J. Yang, L. Shen, N. Sun, H.-S. Lee, and S. Han, ``GCN-RL circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learning,'' Proc. of 2020 57th ACM/IEEE Design Automation Conference (DAC), IEEE, pp. 1-6, 2020.DOI
7 
W. Cao, J. Gao, T. Ma, R. Ma, M. Benosman, and X. Zhang, ``Rose-opt: Robust and efficient analog circuit parameter optimization with knowledge-infused reinforcement learning,'' IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems, vol. 44, no. 2, pp. 627-640, 2025.DOI
8 
K. Yamamoto and N. Takai, ``GNN-OPT: Enhancing automated circuit design optimization with graph neural networks,'' IEICE Transactions on Fundamentals, vol. 108, no. 5, pp. 687-689, 2025.DOI
9 
J. Schmidhuber, ``Formal theory of creativity, fun, and intrinsic motivation (1990-2010),'' IEEE transactions on Autonomous Mental Development, vol. 2, no. 3, pp. 230-247, 2010DOI
10 
A. L. Strehl and M. L. Littman, ``An analysis of modelbased interval estimation for markov decision processes,'' Journal of Computer and System Sciences, vol. 74, no. 8, pp. 1309-1331, 2008.DOI
11 
M. G. Bellemare, S. Srinivasan, G. Ostrovski, T. Schaul, D. Saxton, and R. Munos, ``Unifying count-based exploration and intrinsic motivation,'' Proc. of the 30th International Conference on Neural Information Processing Systems, pp. 1479-1487, 2016.DOI
12 
Y. Burda, H. Edwards, D. Pathak, A. Storkey, T. Darrell, and A. A. Efros, ``Large-scale study of curiosity-driven learning,'' arXiv preprint arXiv:1808.04355, 2018.DOI
13 
D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell, ``Curiosity-driven exploration by self-supervised prediction,'' Proc. of International Conference on Machine Learning, pp. 2778-2787, PMLR, 2017.DOI
14 
Y. Burda, H. Edwards, A. Storkey, and O. Klimov, ``Exploration by random network distillation,'' Proc. of Seventh International Conference on Learning Representations, pp. 1-17, 2019.DOI
15 
K. Sohn, X. Yan, and H. Lee, ``Learning structured output representation using deep conditional generative models,'' Proc. of the 29th International Conference on Neural Information Processing Systems, vol. 2, pp. 3483-3491, 2015.DOI
16 
W. Zhou, S. Bajracharya, and D. Held, ``PLAS: Latent action space for offline reinforcement learning,'' Proc. of Conference on Robot Learning, pp. 1719-1735, PMLR, 2021.DOI
17 
S. Rezaeifar, R. Dadashi, N. Vieillard, L. Hussenot, O. Bachem, O. Pietquin, and M. Geist, ``Offline reinforcement learning as anti-exploration,'' Proc. of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 8106-8114, 2022.DOI
18 
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, ``Proximal policy optimization algorithms,'' arXiv preprint arXiv:1707.06347, 2017.DOI
19 
D. P. Kingma, ``Adam: A method for stochastic optimization,'' arXiv preprint arXiv:1412.6980, 2014.DOI
20 
J. Mockus, ``The application of bayesian methods for seeking the extremum,'' Towards Global Optimization, vol. 2, 117, 1998.URL
21 
T. Zhang, H. Xu, X. Wang, Y. Wu, K. Keutzer, J. E. Gonzalez, and Y. Tian, ``Noveld: A simple yet effective exploration criterion,'' Advances in Neural Information Processing Systems, vol. 34, pp. 25217-25230, 2021.DOI
22 
Y. Wang, M. Orshansky, and C. Caramanis, ``Enabling efficient analog synthesis by coupling sparse regression and polynomial optimization,'' Proc. of the 51st Annual Design Automation Conference, pp. 1-6, 2014.DOI
23 
Ignacio M. Villarreal, ``NGSPYCE: Python bindings for the Ngspice simulation engine,'' 2025. Accessed: 2025-02-10.URL