Projects | Zeheng Wang's page

Advanced semiconductor devices

Wed, 27 Apr 2016 00:00:00 +0000

The “Advanced Semiconductor Devices” project is focused on the design and development of state-of-the-art devices using next-generation materials such as GaN (Gallium Nitride), SiC (Silicon Carbide), Ga2O3 (Gallium Oxide), and various 2D materials. This initiative aims to exploit the unique properties of these advanced materials to create semiconductor devices that surpass traditional silicon-based devices in aspects such as speed, efficiency, and tolerance to extreme environments.

Key Aspects of the Project:

TCAD Simulation for Various Materials: The project emphasizes TCAD (Technology Computer-Aided Design) simulations to predict the behavior of devices made from different advanced materials. This phase involves innovative design strategies to enhance multi-functionality and performance, optimizing device architecture for each material to improve electron mobility, reduce resistance, and achieve faster switching speeds, leading to enhanced energy efficiency and overall performance.
Advanced Fabrication Techniques Across Materials: The project explores new fabrication techniques for devices using GaN, SiC, Ga2O3, and 2D materials. This includes developing methods for producing layers of these materials with minimal defects and superior uniformity. Advancements in fabrication are key for the reliable performance of devices, ensuring consistency and quality in production.
Physical Mechanisms and Reliability in Diverse Materials: Investigating the physical mechanisms underlying the operation of devices made from these advanced materials is a crucial aspect. Understanding the physics of electron behavior, material properties, and thermal dynamics across different materials informs strategies for enhancing device reliability and longevity. This is vital for their application in high-power and high-frequency environments.
Circuit Integration and Diverse Applications: The project aims to integrate these advanced devices into high-frequency electronic circuits, expanding their application in areas such as wireless communication, radar systems, and satellite communications. These devices offer invaluable benefits in efficiency and speed for modern high-frequency applications, contributing to the advancement of electronic systems.

Future Directions: The project plans to continue refining technologies for various advanced semiconductor materials, exploring new design paradigms, and advancing fabrication techniques. The ultimate goal is to mainstream these advanced semiconductor devices for a broad spectrum of high-performance electronic applications, leading to more efficient and powerful electronic devices.

This initiative aligns with the global focus on advanced semiconductor technologies, underscoring their critical role in driving future electronic innovations.

My recent publications in this field:

Advanced device design and fabrication

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.
Z. Wang et al., “A high-performance tunable LED-compatible current regulator using an integrated voltage nanosensor,” IEEE Transactions on Electron Devices, vol. 66, no. 4, pp. 1917–1923, Apr. 2019, doi: 10.1109/TED.2019.2899756.

Device TCAD simulation

Z. Wang, “Proposal of a novel recess-free enhancement-mode AlGaN/GaN HEMT with field-assembled structure: a simulation study,” Journal of Computational Electronics, vol. 18, no. 4, pp. 1251–1258, Dec. 2019, doi: 10.1007/s10825-019-01383-7.
Z. Wang, D. Yang, J. Cao, F. Wang, and Y. Yao, “A novel technology for turn-on voltage reduction of high-performance lateral heterojunction diode with source-gate shorted anode,” Superlattices and Microstructures, vol. 125, no. September 2018, pp. 144–150, Jan. 2019, doi: 10.1016/j.spmi.2018.11.003.

Device physics

Z. Wang, D. Yang, J. Shi, and Y. Yao, “Approaching ultra-low turn-on voltage in GaN lateral diode,” Semiconductor Science and Technology, vol. 36, no. 1, p. 014003, Jan. 2020, doi: 10.1088/1361-6641/abc70b.
Z. Wang and L. Li, “Two-dimensional polarization doping of GaN heterojunction and its potential for realizing lateral p–n junction devices,” Appl. Phys. A, vol. 128, no. 8, p. 672, Aug. 2022, doi: 10.1007/s00339-022-05824-2.
F. Wang, W. Chen, R. Sun, Z. Wang, Q. Zhou, and B. Zhang, “An analytical model on the gate control capability in p-GaN gate AlGaN/GaN high-electron-mobility transistors considering buffer acceptor traps,” Journal of Physics D: Applied Physics, vol. 54, no. 9, p. 095107, Mar. 2021, doi: 10.1088/1361-6463/abc504.

AI for herbal medicine

Wed, 27 Apr 2016 00:00:00 +0000

The “AI for Traditional Chinese Herbal Medicine” project is an innovative intersection of artificial intelligence (AI) and traditional Chinese medicine (TCM), aiming to revolutionize the way herbal remedies are studied, developed, and utilized. This project’s motivation stems from the complex nature of TCM, where the efficacy of herbal treatments depends on a multitude of factors, including plant species, harvest time, and preparation methods. AI, with its robust data processing and pattern recognition capabilities, offers a powerful tool to unravel these complexities.

The core of this project involves using machine learning algorithms and data analytics to analyze vast amounts of historical and contemporary data on herbal medicines. This includes patient outcomes, herbal compositions, and their pharmacological effects. By doing so, the project seeks to identify effective herbal combinations, predict therapeutic effects, and personalize treatments for individual patients.

Significantly, this project also focuses on digitizing and translating ancient TCM texts and pharmacopoeias, converting centuries-old knowledge into accessible data for AI analysis. This fusion of traditional knowledge and modern technology not only preserves ancient wisdom but also enhances it with new insights and applications.

The work completed so far in this project has shown promising results in areas like identifying potential herbal treatments for modern diseases, optimizing herbal formulations, and even aiding in new drug discovery. The next steps involve further refining the AI models, expanding the herbal database, and integrating these findings into clinical practice to provide more effective and personalized healthcare solutions..

My recent publications in this field:

Y. Yao et al., “An ontology-based artificial intelligence model for medicine side-effect prediction: taking traditional chinese medicine as an example,” Computational and Mathematical Methods in Medicine, vol. 2019, pp. 1–7, Oct. 2019, doi: 10.1155/2019/8617503.
Z. Wang, L. Li, J. Yan, and Y. Yao, “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.
Z. Wang, L. Li, M. Song, J. Yan, J. Shi, and Y. Yao, “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.

AI for semiconductor device modeling

Wed, 27 Apr 2016 00:00:00 +0000

The “AI for Semiconductor Device Modeling” project is an ambitious endeavor that harnesses the power of artificial intelligence to revolutionize the field of semiconductor device design and analysis. The primary motivation behind this initiative is to overcome the increasing complexity and scalability challenges in semiconductor device modeling, which traditional computational methods struggle to address efficiently.

At the heart of this project is the application of advanced machine learning algorithms and data-driven approaches to model the behavior of semiconductor devices. These AI models are capable of analyzing vast datasets, derived from both experimental measurements and theoretical simulations, to predict device performance, optimize design parameters, and identify novel materials and structures for semiconductor fabrication.

One of the key achievements of this project has been the development of predictive models that significantly reduce the time and resources needed for device characterization and simulation. By leveraging AI, the project has enabled more accurate and rapid prototyping, leading to faster innovation cycles in semiconductor technology.

Looking forward, the project aims to integrate AI more deeply into the semiconductor manufacturing process, enhancing automation and precision in fabrication techniques. The ultimate goal is to establish a new paradigm in semiconductor design where AI-driven insights guide the development of more powerful, energy-efficient, and miniaturized electronic devices.

My recent publications in this field:

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.
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.

Semiconductor-based quantum computing devices/chips

Wed, 27 Apr 2016 00:00:00 +0000

The Jellybean Quantum project focuses on developing novel quantum dots within a silicon framework, targeting advancements in quantum computing. The motivation behind this project lies in the need for effective qubit coupling and manageable quantum state manipulation, crucial for scalable quantum computing systems. The “jellybean” shaped quantum dots are a unique design innovation, intended to enhance interaction between qubits and facilitate complex quantum computations. The significance of this project is rooted in its potential to bridge the gap between theoretical quantum computing models and practical, scalable quantum processors. Work completed so far has involved theoretical modeling and experimental validation of the jellybean quantum dots, showcasing their suitability for complex quantum operations. Future directions include refining these quantum devices for more efficient qubit coupling and exploring broader applications in on-chip quantum chemistry and quantum information processing.