Dr Zeheng Wang

Zeheng is a CERC Fellow, appointed through the Impossible Without You campaign. He earned his PhD in Si quantum computing devices from CQC2T, UNSW, and also has significant experience in semiconductor fabrication, physics, and simulation, machine learning, and AI for medicine. Zeheng has published over 50 scientific papers in top-tier venues, including Nature, Adv. Mat. (cover article), Adv. Sci. (cover article), Energy & Environmental Science, IEEE Electron Device Letters, and IEEE Transactions on Electron Devices, as well as flagship conferences such as IEEE IEDM and IEEE ISPSD.

Zeheng's expertise is recognized by his service as an editorial position for Plos One (JIF2.9), Micromachines (JIF3.5) and Applied Research (Wiley, ESCI journal). He is also an active reviewer for many leading journals published by Wiley, Springer, Elsevier, IOP, IEEE, IET, and IEICE. His open-source online service for semiconductor fabrication has been used by over 300 researchers worldwide. Additionally, he invented two patents in the field of semiconductor hardware.

Zeheng's current research interests include quantum artificial intelligence, artificial intelligence for science (AI4Science, particularly in materials, electronics, and medicines), and semiconductor devices.

Full publication list: https://scholar.google.com/citations?user=ed7_BdgAAAAJ

Key scientific contributions out of all 50 publications in top venues:

[In quantum computing devices and quantum machine learning in NISQ devices]
[1] Z. Wang et al., “Jellybean Quantum Dots in Silicon for Qubit Coupling and On‐Chip Quantum Chemistry,” Adv. Mater., vol. 35, no. 19, p. 2208557, May 2023, doi: 10.1002/adma.202208557.
[2] Z. Wang et al., Self-Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays. Adv. Sci. 2025, 12, 2411573. https://doi.org/10.1002/advs.202411573.
[3] Z. Wang , F. Wang, L. Li, Z. Wang, T. van der Laan, R. C. C. Leon, J.-K. Huang, M. Usman, Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact. Adv. Sci. 2025, e06213. https://doi.org/10.1002/advs.2025062135

[In machine learning for semiconductors]
[4] Z. Wang et al., “Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques,” IEEE Trans. Electron Devices, pp. 1–9, 2023, doi: 10.1109/TED.2023.3307051.
[5] Z. Wang, L. Li, and Y. Yao, “A machine learning-assisted model for GaN ohmic contacts regarding the fabrication processes,” IEEE Trans. Electron Devices, vol. 68, no. 5, pp. 2212–2219, May 2021, doi: 10.1109/TED.2021.3063213.
[6] Z. Wang et al., "Blue and Green-Mode Energy-Efficient Nanoparticle-Based Chemiresistive Sensor Array Realized by Rapid Ensemble Learning", ACS Appl. Nano Mater. 2024, 7, 21, 24437–24446, doi: 10.1021/acsanm.4c04060

[In design and fabrication of semiconductor devices]
[7] Z. Wang et al., “A high-performance tunable LED-compatible current regulator using an integrated voltage nanosensor,” IEEE Trans. Electron Devices, vol. 66, no. 4, pp. 1917–1923, Apr. 2019, doi: 10.1109/TED.2019.2899756.
[8] Z. Wang, D. Yang, J. Shi, and Y. Yao, “Approaching ultra-low turn-on voltage in GaN lateral diode,” Semicond. Sci. Technol., vol. 36, no. 1, p. 014003, Jan. 2020, doi: 10.1088/1361-6641/abc70b.
[9] J.-K. Huang et al., “High-κ perovskite membranes as insulators for two-dimensional transistors,” Nature, vol. 605, no. 7909, pp. 262–267, May 2022, doi: 10.1038/s41586-022-04588-2.

[In medicine and clinical decision support]
[10] Z. Wang et al., “Evaluating the traditional chinese medicine (TCM) officially recommended in china for COVID-19 using ontology-based side-effect prediction framework (OSPF) and deep learning,” Journal of Ethnopharmacology, vol. 272, p. 113957, Feb. 2021, doi: 10.1016/j.jep.2021.113957.
[11] Z. Wang et al., “Approaching high-accuracy side effect prediction of traditional chinese medicine compound prescription using network embedding and deep learning,” IEEE Access, vol. 8, pp. 82493–82499, 2020, doi: 10.1109/ACCESS.2020.2991750.
[12] G. Li, C. Li, C. Wang, and Z. Wang, “Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital,” PLoS ONE, vol. 19, no. 2, p. e0298328, Feb. 2024, doi: 10.1371/journal.pone.0298328.
[13] Y. Wang, C. Li, and Z. Wang, “Advancing Precision Medicine: VAE Enhanced Predictions of Pancreatic Cancer Patient Survival in Local Hospital,” IEEE Access, vol. 12, pp. 3428–3436, 2024, doi: 10.1109/ACCESS.2023.3348810.