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

    •  EVENT   Prof. Melanie Zeilinger of ETH to give keynote at MERL's Virtual Open House
      Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
      Speaker: Prof. Melanie Zeilinger, ETH
      Location: Virtual Event
      Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information Security
      Brief
      • MERL is excited to announce the second keynote speaker for our Virtual Open House 2021:
        Prof. Melanie Zeilinger from ETH .

        Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).

        Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Zeilinger's talk is scheduled for 3:15pm - 3:45pm (EST).

        Registration: https://mailchi.mp/merl/merlvoh2021

        Keynote Title: Control Meets Learning - On Performance, Safety and User Interaction

        Abstract: With increasing sensing and communication capabilities, physical systems today are becoming one of the largest generators of data, making learning a central component of autonomous control systems. While this paradigm shift offers tremendous opportunities to address new levels of system complexity, variability and user interaction, it also raises fundamental questions of learning in a closed-loop dynamical control system. In this talk, I will present some of our recent results showing how even safety-critical systems can leverage the potential of data. I will first briefly present concepts for using learning for automatic controller design and for a new safety framework that can equip any learning-based controller with safety guarantees. The second part will then discuss how expert and user information can be utilized to optimize system performance, where I will particularly highlight an approach developed together with MERL for personalizing the motion planning in autonomous driving to the individual driving style of a passenger.
    •  
    •  EVENT   Prof. Ashok Veeraraghavan of Rice University to give keynote at MERL's Virtual Open House
      Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
      Speaker: Prof. Ashok Veeraraghavan, Rice University
      Location: Virtual Event
      Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information Security
      Brief
      • MERL is excited to announce the first keynote speaker for our Virtual Open House 2021:
        Prof. Ashok Veeraraghavan from Rice University.

        Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).

        Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Veeraraghavan's talk is scheduled for 1:15pm - 1:45pm (EST).

        Registration: https://mailchi.mp/merl/merlvoh2021

        Keynote Title: Computational Imaging: Beyond the limits imposed by lenses.

        Abstract: The lens has long been a central element of cameras, since its early use in the mid-nineteenth century by Niepce, Talbot, and Daguerre. The role of the lens, from the Daguerrotype to modern digital cameras, is to refract light to achieve a one-to-one mapping between a point in the scene and a point on the sensor. This effect enables the sensor to compute a particular two-dimensional (2D) integral of the incident 4D light-field. We propose a radical departure from this practice and the many limitations it imposes. In the talk we focus on two inter-related research projects that attempt to go beyond lens-based imaging.

        First, we discuss our lab’s recent efforts to build flat, extremely thin imaging devices by replacing the lens in a conventional camera with an amplitude mask and computational reconstruction algorithms. These lensless cameras, called FlatCams can be less than a millimeter in thickness and enable applications where size, weight, thickness or cost are the driving factors. Second, we discuss high-resolution, long-distance imaging using Fourier Ptychography, where the need for a large aperture aberration corrected lens is replaced by a camera array and associated phase retrieval algorithms resulting again in order of magnitude reductions in size, weight and cost. Finally, I will spend a few minutes discussing how the wholistic computational imaging approach can be used to create ultra-high-resolution wavefront sensors.
    •  

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  • Internships

    • MD1714: Electric Motor Design

      MERL is seeing a motivated and qualified individual to conduct research on electric machine design, prototype, and experiment tests. The ideal candidate should have solid background and demonstrated research experience in electric machine theory, design analysis, motor drives, and control. Hands-on experiences on electric motor design and prototyping, test bench set up, and experiment measurements are required. Senior Ph.D. students in electrical engineering or mechanical engineering with related expertise are encouraged to apply. Start date for this internship is flexible. This internship requires work that can only be done at MERL.

    • MD1648: THz Electronic Sensing

      MERL is looking for a senior Ph.D. student to join our team to conduct application-motivated research and experiments. The candidate must have hands-on practical lab experiment experience on millimeter-wave, sub-THz, or THz for sensing, radar, and other applications. Skills of using RF/Microwave Lab equipment are necessary. Knowledge of solid-state device physics, high frequency, and high speed integrated circuit (IC) chip design, and signal processing is desired. The internship is expected to be 3-6 months, starting date is flexible after September. This internship requires work that can only be done at MERL.

    • SP1747: Learning for Inverse Problems

      The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that solve inverse problems in computational sensing that incorporate deep learning architectures for a variety of sensing applications. The project goal is to improve the performance and develop an analysis of algorithms used for inverse problems by incorporating new tools from machine learning and artificial intelligence. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, large-scale optimization, plug-and-play priors, learning-based modeling for imaging, learning theory for computational imaging. 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.


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  • Recent Publications

    •  Wang, B., Shin, K.-H., Hidaka, Y., Kondo, S., Arita, H., Ito, K., "Analytical Magnetic Model for Variable-Flux Interior Permanent Magnet Synchronous Motors", IEEE Energy Conversion Congress and Exposition (ECCE), October 2021.
      BibTeX TR2021-123 PDF
      • @inproceedings{Wang2021oct2,
      • author = {Wang, Bingnan and Shin, Kyung-Hun and Hidaka, Yuki and Kondo, Shota and Arita, Hideaki and Ito, Kazumasa},
      • title = {Analytical Magnetic Model for Variable-Flux Interior Permanent Magnet Synchronous Motors},
      • booktitle = {IEEE Energy Conversion Congress and Exposition (ECCE)},
      • year = 2021,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2021-123}
      • }
    •  Wang, B., Zhou, L., Wang, H., Lin, C., "Analytical Modeling and Design Optimization of a Vernier Permanent Magnet Motor", IEEE Energy Conversion Congress and Exposition (ECCE), October 2021.
      BibTeX TR2021-124 PDF
      • @inproceedings{Wang2021oct3,
      • author = {Wang, Bingnan and Zhou, Lei and Wang, Hongyu and Lin, Chungwei},
      • title = {Analytical Modeling and Design Optimization of a Vernier Permanent Magnet Motor},
      • booktitle = {IEEE Energy Conversion Congress and Exposition (ECCE)},
      • year = 2021,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2021-124}
      • }
    •  Tanaka, R., Nabi, S., Nonaka, M., "Airflow Optimization for Room Air Conditioners", Building Simulation 2021 Conference, September 2021.
      BibTeX TR2021-106 PDF
      • @inproceedings{Tanaka2021sep,
      • author = {Tanaka, Ryuta and Nabi, Saleh and Nonaka, Mio},
      • title = {Airflow Optimization for Room Air Conditioners},
      • booktitle = {Building Simulation 2021 Conference},
      • year = 2021,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2021-106}
      • }
    •  Li, K., Yagyu, E., Sato, H., Teo, K.H., Rakheja, S., "Compact modeling of gate leakage phenomenon in GaN HEMTs", IEEE Transactions on Electron Devices, DOI: 10.23919/​SISPAD49475.2020.9241666, June 2021.
      BibTeX TR2021-079 PDF
      • @article{Li2021jun,
      • author = {Li, Kexin and Yagyu, Eiji and Sato, Hisashi and Teo, Koon Hoo and Rakheja, Shaloo},
      • title = {Compact modeling of gate leakage phenomenon in GaN HEMTs},
      • journal = {IEEE Transactions on Electron Devices},
      • year = 2021,
      • month = jun,
      • doi = {10.23919/SISPAD49475.2020.9241666},
      • url = {https://www.merl.com/publications/TR2021-079}
      • }
    •  Zhou, L., Guo, F., Wang, H., Wang, B., "High-Torque Direct-Drive Machine with Combined Axial- and Radial-flux Out-runner Vernier Permanent Magnet Motor", International Electric Machine & Drives Conference (IEMDC), DOI: 10.1109/​IEMDC47953.2021.9449499, May 2021, pp. 1-8.
      BibTeX TR2021-050 PDF
      • @inproceedings{Zhou2021may,
      • author = {Zhou, Lei and Guo, Feng and Wang, Hongyu and Wang, Bingnan},
      • title = {High-Torque Direct-Drive Machine with Combined Axial- and Radial-flux Out-runner Vernier Permanent Magnet Motor},
      • booktitle = {2021 IEEE International Electric Machines Drives Conference (IEMDC)},
      • year = 2021,
      • pages = {1--8},
      • month = may,
      • publisher = {IEEE},
      • doi = {10.1109/IEMDC47953.2021.9449499},
      • url = {https://www.merl.com/publications/TR2021-050}
      • }
    •  Zhou, L., Wang, B., Lin, C., Miyoshi, M., Inoue, H., "Static Eccentricity Fault Detection for PSH-type Induction Motors Considering High-order Air Gap Permeance Harmonics", International Electric Machine & Drives Conference (IEMDC), DOI: 10.1109/​IEMDC47953.2021.9449496, May 2021, pp. 1-7.
      BibTeX TR2021-051 PDF
      • @inproceedings{Zhou2021may2,
      • author = {Zhou, Lei and Wang, Bingnan and Lin, Chungwei and Miyoshi, Masahito and Inoue, hiroshi},
      • title = {Static Eccentricity Fault Detection for PSH-type Induction Motors Considering High-order Air Gap Permeance Harmonics},
      • booktitle = {2021 IEEE International Electric Machines Drives Conference (IEMDC)},
      • year = 2021,
      • pages = {1--7},
      • month = may,
      • publisher = {IEEE},
      • doi = {10.1109/IEMDC47953.2021.9449496},
      • url = {https://www.merl.com/publications/TR2021-051}
      • }
    •  Lin, C., Ma, Y., Sels, D., "Optimal Control for Quantum Metrology via Pontryagin's principle", Physical Review, DOI: 10.1103/​PhysRevA.103.052607, Vol. 103, No. 5, pp. 052607, May 2021.
      BibTeX TR2021-067 PDF
      • @article{Lin2021may,
      • author = {Lin, Chungwei and Ma, Yanting and Sels, Dries},
      • title = {Optimal Control for Quantum Metrology via Pontryagin's principle},
      • journal = {Physical Review},
      • year = 2021,
      • volume = 103,
      • number = 5,
      • pages = 052607,
      • month = may,
      • doi = {10.1103/PhysRevA.103.052607},
      • url = {https://www.merl.com/publications/TR2021-067}
      • }
    •  Ma, R., Komatsuszaki, Y., Benosman, M., Yamanaka, K., Shinjo, S., "A New Frontier for Power Amplifier enabled by Machine Learning", Microwave Journal, No. 4, pp. 22-32, April 2021.
      BibTeX TR2021-030 PDF
      • @article{Ma2021apr,
      • author = {Ma, Rui and Komatsuszaki, Yuji and Benosman, Mouhacine and Yamanaka, Koji and Shinjo, Shintaro},
      • title = {A New Frontier for Power Amplifier enabled by Machine Learning},
      • journal = {Microwave Journal},
      • year = 2021,
      • number = 4,
      • pages = {22--32},
      • month = apr,
      • issn = {0192-6225},
      • url = {https://www.merl.com/publications/TR2021-030}
      • }
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  • Videos