Applied Physics

From first-principles modeling to device designs.

Our research in this area uses physics to develop new technologies or solve an engineering problem, including optimal design of freeform optics, metamaterials, photonic and solid-state semiconductor devices; the modeling and analysis of electro-magnetic systems and studies on superconductivity and magnets.

  • Researchers

  • News & Events


    See All News & Events for Applied Physics
  • Internships

    • MD1381: Electric Motor Design

      MERL is seeking a motivated and qualified individual to conduct research in design, modeling, and simulation of electrical machines. The ideal candidate should have solid backgrounds in modeling (including model reduction)/co-simulation of electromagnetics and thermal dynamics of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and real-time control experiments involving motor drives is a strong plus. Senior Ph.D. students in electrical or mechanical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3-6 months.

    • SP1585: Three dimensional Imaging from Compton Camera

      The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that reconstruct a three dimensional distribution of a radioactive source when observed using a Compton camera. The project goal is to improve the performance and develop an uncertainty analysis of these algorithms. Ideal candidates should be Ph.D. students and have solid background and publication record in 3D Compton imaging. Experience in computational tomography, imaging inverse problems, and large-scale optimization is also preferred. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • SP1513: Designing and optimizing photonic devices using deep learning

      MERL is seeking a highly motivated, qualified individual to join our internship program and conduct research in the area of photonic and nanophotonic device design and optimization using deep learning. The ideal candidate should have a strong background in the simulation (such as Lumerical FDTD), design, and testing of devices for optical communications and/or optical computing, as well as hands-on experience in deep learning (such as autoencoders and GANs using Tensorflow/Keras/PyTorch). Experience in silicon photonics, photonic crystal, plasmonicss, metasurface optics, optimization algorithms, machine learning, quantum computing, photonic device fabrication/measurements, and mask designs for InP and silicon photonic MPW would be considered an asset. Candidates who hold a Ph.D. or in their senior years of a Ph.D. program are encouraged to apply.


    See All Internships for Applied Physics
  • Recent Publications

    •  Teo, K.H., Chowdhury, N., Zhang, Y., Palacios, T., Yamanaka, K., Yamaguchi, Y., "Recent Development in 2D and 3D GaN devices for RF and Power Electronics Applications", IEEE International Symposium on Radio-Frequency Integration Technology (RFIT), November 2020.
      BibTeX TR2020-162 PDF
      • @inproceedings{Teo2020nov,
      • author = {Teo, Koon Hoo and Chowdhury, Nadim and Zhang, Yuhao and Palacios, Tomas and Yamanaka, Koji and Yamaguchi, Yutaro},
      • title = {Recent Development in 2D and 3D GaN devices for RF and Power Electronics Applications},
      • booktitle = {IEEE International Symposium on Radio-Frequency Integration Technology (RFIT)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-162}
      • }
    •  Shin, K.-H., Wang, B., "Semi-Analytical Modeling for Interior Permanent Magnet Synchronous Machines Considering Permeability of Rotor Core", International Conference on Electrical Machines and Systems (ICEMS), November 2020.
      BibTeX TR2020-149 PDF
      • @inproceedings{Shin2020nov,
      • author = {Shin, Kyung-Hun and Wang, Bingnan},
      • title = {Semi-Analytical Modeling for Interior Permanent Magnet Synchronous Machines Considering Permeability of Rotor Core},
      • booktitle = {International Conference on Electrical Machines and Systems (ICEMS)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-149}
      • }
    •  Wang, B., Hotta, A., "Contactless Eddy Current Sensing for Carbon Fiber Reinforced Polymer Defect Detection", Biennial IEEE Conference on Electromagnetic Field Computation (CEFC), November 2020.
      BibTeX TR2020-148 PDF
      • @inproceedings{Wang2020nov2,
      • author = {Wang, Bingnan and Hotta, Akira},
      • title = {Contactless Eddy Current Sensing for Carbon Fiber Reinforced Polymer Defect Detection},
      • booktitle = {Biennial IEEE Conference on Electromagnetic Field Computation (CEFC)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-148}
      • }
    •  Tian, N., Fang, H., Chen, J., Wang, Y., "Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2020.2976036, pp. 1-15, April 2020.
      BibTeX TR2020-035 PDF
      • @article{Tian2020apr,
      • author = {Tian, Ning and Fang, Huazhen and Chen, Jian and Wang, Yebin},
      • title = {Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation},
      • journal = {IEEE Transactions on Control Systems Technology},
      • year = 2020,
      • pages = {1--15},
      • month = apr,
      • doi = {10.1109/TCST.2020.2976036},
      • url = {https://www.merl.com/publications/TR2020-035}
      • }
    •  Kojima, K., TaherSima, M., Koike-Akino, T., Jha, D., Tang, Y., Parsons, K., Sang, F., Klamkin, J., "Deep Neural Networks for Designing Integrated Photonics", Optical Fiber Communication Conference and Exposition (OFC), DOI: 10.1364/OFC.2020.Th1A.6, March 2020.
      BibTeX TR2020-057 PDF
      • @inproceedings{Kojima2020mar,
      • author = {Kojima, Keisuke and TaherSima, Mohammad and Koike-Akino, Toshiaki and Jha, Devesh and Tang, Yingheng and Parsons, Kieran and Sang, Fengqiao and Klamkin, Jonathan},
      • title = {Deep Neural Networks for Designing Integrated Photonics},
      • booktitle = {Optical Fiber Communication Conference and Exposition (OFC)},
      • year = 2020,
      • month = mar,
      • publisher = {OSA},
      • doi = {10.1364/OFC.2020.Th1A.6},
      • isbn = {978-1-943580-71-2},
      • url = {https://www.merl.com/publications/TR2020-057}
      • }
    •  Lin, C., Sels, D., Wang, Y., "Time-optimal Control of a Dissipative Qubit", Physical Review, DOI: 10.1103/PhysRevA.101.022320, Vol. 101, No. 2, pp. 022320, February 2020.
      BibTeX TR2020-023 PDF
      • @article{Lin2020feb,
      • author = {Lin, Chungwei and Sels, Dries and Wang, Yebin},
      • title = {Time-optimal Control of a Dissipative Qubit},
      • journal = {Physical Review},
      • year = 2020,
      • volume = 101,
      • number = 2,
      • pages = 022320,
      • month = feb,
      • doi = {10.1103/PhysRevA.101.022320},
      • url = {https://www.merl.com/publications/TR2020-023}
      • }
    •  Teo, K.H., "Ferroelectric memory field-effect transistors using CVD monolayer MoS2 as resistive switching channel", Applied Physics Letters, DOI: 10.1063/1.5129963, Vol. Appl. Phys. Lett. 116, January 2020.
      BibTeX TR2020-012 PDF
      • @article{Teo2020jan,
      • author = {Teo, Koon Hoo},
      • title = {Ferroelectric memory field-effect transistors using CVD monolayer MoS2 as resistive switching channel},
      • journal = {Applied Physics Letters},
      • year = 2020,
      • volume = {Appl. Phys. Lett. 116},
      • month = jan,
      • doi = {10.1063/1.5129963},
      • url = {https://www.merl.com/publications/TR2020-012}
      • }
    See All Publications for Applied Physics
  • Videos