Electronic and Photonic Devices

Pursuing theoretical and experimental research for next generation devices.

We explore various device technologies, material science and device architectures to dramatically improve power and RF device performance to achieve higher efficiency, high linearity and much wider frequency band. We develop novel photonic integrated circuits to improve performance and reduce cost in optical communications applications.

  • Researchers

  • Awards


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  • News & Events

    •  NEWS   Research on Intelligent Power Amplifier is Cover Story of Microwave Journal
      Date: April 15, 2021
      MERL Contacts: Mouhacine Benosman; Rui Ma; Koon Hoo Teo
      Research Areas: Communications, Electronic and Photonic Devices, Machine Learning
      Brief
      • The cover article in the April issue of Microwave Journal features MERL and MELCO's invited paper entitled "A New Frontier for Power Amplifiers Enabled by Machine Learning". Our recent research applying ML for optimizing operating conditions of advanced power amplifier designs is highlighted.

        Since 1958, Microwave Journal has been the leading source for information about RF and Microwave technology, design techniques, news, events and educational information. Microwave Journal reaches 50,000 qualified readers monthly with a print magazine that has a global reach.
    •  
    •  TALK   Prof. Pere Gilabert gave an invited talk at MERL on Machine Learning for Digital Predistortion Linearization of High Efficient Power Amplifier
      Date & Time: Tuesday, February 16, 2021; 11:00-12:00
      Speaker: Prof. Pere Gilabert, Universitat Politecnica de Catalunya, Barcelona, Spain
      MERL Host: Rui Ma
      Research Areas: Communications, Electronic and Photonic Devices, Machine Learning, Signal Processing
      Brief
      • Digital predistortion (DPD) linearization is the most common and spread solution to cope with power amplifiers (PA) inherent linearity versus efficiency trade-off. The use of new radio 5G spectrally efficient signals with high peak-to-average power ratios (PAPR) occupying wider bandwidths only aggravates such compromise. When considering wide bandwidth signals, carrier aggregation or multi-band configurations in high efficient transmitter architectures, such as Doherty PAs, load-modulated balanced amplifiers, envelope tracking PAs or outphasing transmitters, the number of parameters required in the DPD model to compensate for both nonlinearities and memory effects can be unacceptably high. This has a negative impact in the DPD model extraction/adaptation, because it increases the computational complexity and drives to over-fitting and uncertainty.
        This talk will discuss the use of machine learning techniques for DPD linearization. The use of artificial neural networks (ANNs) for adaptive DPD linearization and approaches to reduce the coefficients adaptation time will be discussed. In addition, an overview on several feature-extraction techniques used to reduce the number of parameters of the DPD linearization system as well as to ensure proper, well-conditioned estimation for related variables will be presented.
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  • Internships

    • SP1504: Coherent Imaging Systems

      MERL is seeking an intern to work on coherent optical imaging. The ideal candidate would be an experienced PhD student or post-graduate researcher working in coherent imaging. The candidate should have a detailed knowledge of optical interferometry and imaging with a focus on either optical coherence tomography, optical coherence microscopy or FMCW LIDAR. Strong programming skills in MATLAB are essential. Experience of working in an optical lab environment is a required. Duration is 3 to 6 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • MD1561: Desgn and fabrication of power devices in power electronics or RF

      MERL is seeking a highly motivated, qualified individual to join our 3-month internship program to carry out research in the area of power electronics and RF semiconductors devices. The ideal candidate should have a significant background in the simulation and design of a 2D and 3D GaN devices using Matlab and TCAD. Proficiency in device semiconductor modeling or hands-on experience in GaN device fabrication processes and a deep knowledge of negative capacitance would be a great asset. Candidates who hold a PhD or in their senior years of a Ph.D. program are encouraged to apply. 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

    •  Tang, Y., Kojima, K., Koike-Akino, T., Wang, Y., Jha, D., Parsons, K., Qi, M., "Nano-Optic Broadband Power Splitter Design via Cycle-Consistent Adversarial Deep Learning", Conference on Lasers and Electro-Optics (CLEO), May 2021.
      BibTeX TR2021-045 PDF Presentation
      • @inproceedings{Tang2021may3,
      • author = {Tang, Yingheng and Kojima, Keisuke and Koike-Akino, Toshiaki and Wang, Ye and Jha, Devesh and Parsons, Kieran and Qi, Minghao},
      • title = {Nano-Optic Broadband Power Splitter Design via Cycle-Consistent Adversarial Deep Learning},
      • booktitle = {Conference on Lasers and Electro-Optics (CLEO)},
      • year = 2021,
      • month = may,
      • url = {https://www.merl.com/publications/TR2021-045}
      • }
    •  Fujihashi, T., Koike-Akino, T., Watanabe, T., Orlik, P.V., "HoloCast+: Hybrid Digital-Analog Transmission for Graceful Point Cloud Delivery with Graph Fourier Transform", IEEE Transactions on Multimedia, DOI: 10.1109/​TMM.2021.3077772, May 2021.
      BibTeX TR2021-043 PDF
      • @article{Fujihashi2021may,
      • author = {Fujihashi, Takuya and Koike-Akino, Toshiaki and Watanabe, Takashi and Orlik, Philip V.},
      • title = {HoloCast+: Hybrid Digital-Analog Transmission for Graceful Point Cloud Delivery with Graph Fourier Transform},
      • journal = {IEEE Transactions on Multimedia},
      • year = 2021,
      • month = may,
      • doi = {10.1109/TMM.2021.3077772},
      • url = {https://www.merl.com/publications/TR2021-043}
      • }
    •  Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., Parsons, K., Meissner, T., Song, B., Sang, F., Yi, X., Klamkin, J., "InP Grating Coupler Design for Vertical Coupling of InP and Silicon Chips," Tech. Rep. TR2021-034, MERL Technical Report, May 2021.
      BibTeX TR2021-034 PDF
      • @techreport{Tang2021may2,
      • author = {Tang, Yingheng and Kojima, Keisuke and Gotoda, Mitsunobu and Nishikawa, Satoshi and Hayashi, Shusaku and Koike-Akino, Toshiaki and Parsons, Kieran and Meissner, Thomas and Song, Bowen and Sang, Fengqiao and Yi, Xiongsheng and Klamkin, Jonathan},
      • title = {InP Grating Coupler Design for Vertical Coupling of InP and Silicon Chips},
      • institution = {MERL Technical Report},
      • year = 2021,
      • month = may,
      • url = {https://www.merl.com/publications/TR2021-034}
      • }
    •  Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., Parsons, K., Meissner, T., Song, B., Sang, F., Yi, X., Klamkin, J., "InP Grating Coupler Design for Vertical Coupling of InP and Silicon Chips," Tech. Rep. TR2021-034, MERL Technical Report, May 2021.
      BibTeX TR2021-034 PDF
      • @techreport{Tang2021may,
      • author = {Tang, Yingheng and Kojima, Keisuke and Gotoda, Mitsunobu and Nishikawa, Satoshi and Hayashi, Shusaku and Koike-Akino, Toshiaki and Parsons, Kieran and Meissner, Thomas and Song, Bowen and Sang, Fengqiao and Yi, Xiongsheng and Klamkin, Jonathan},
      • title = {InP Grating Coupler Design for Vertical Coupling of InP and Silicon Chips},
      • institution = {MERL Technical Report},
      • year = 2021,
      • month = may,
      • url = {https://www.merl.com/publications/TR2021-034}
      • }
    •  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}
      • }
    •  Teo, K.H., "International Conference on Electron Device Meeting Report," Tech. Rep. TR2021-017, Mitsubishi Electric Research Laboratories, March 2021.
      BibTeX TR2021-017 PDF
      • @techreport{Teo2021mar,
      • author = {Teo, Koon Hoo},
      • title = {International Conference on Electron Device Meeting Report},
      • institution = {for MERL Tech Report},
      • year = 2021,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2021-017}
      • }
    •  Kojima, K., Tang, Y., Koike-Akino, T., Wang, Y., Jha, D., TaherSima, M., Parsons, K., "Application of Deep Learning for Nanophotonic Device Design", SPIE Photonics West, Bahram Jalali and Ken-ichi Kitayama, Eds., DOI: 10.1117/​12.2579104, March 2021.
      BibTeX TR2020-182 PDF Video
      • @inproceedings{Kojima2021mar,
      • author = {Kojima, Keisuke and Tang, Yingheng and Koike-Akino, Toshiaki and Wang, Ye and Jha, Devesh and TaherSima, Mohammad and Parsons, Kieran},
      • title = {Application of Deep Learning for Nanophotonic Device Design},
      • booktitle = {SPIE Photonics West},
      • year = 2021,
      • editor = {Bahram Jalali and Ken-ichi Kitayama},
      • month = mar,
      • publisher = {SPIE},
      • doi = {10.1117/12.2579104},
      • url = {https://www.merl.com/publications/TR2020-182}
      • }
    •  Kojima, K., TaherSima, M., Koike-Akino, T., Jha, D., Tang, Y., Wang, Y., Parsons, K., "Deep Neural Networks for Inverse Design of Nanophotonic Devices", IEEE Journal of Lightwave Technology, DOI: 10.1109/​JLT.2021.3050083, January 2021.
      BibTeX TR2021-001 PDF
      • @article{Kojima2021jan,
      • author = {Kojima, Keisuke and TaherSima, Mohammad and Koike-Akino, Toshiaki and Jha, Devesh and Tang, Yingheng and Wang, Ye and Parsons, Kieran},
      • title = {Deep Neural Networks for Inverse Design of Nanophotonic Devices},
      • journal = {IEEE Journal of Lightwave Technology},
      • year = 2021,
      • month = jan,
      • doi = {10.1109/JLT.2021.3050083},
      • issn = {1558-2213},
      • url = {https://www.merl.com/publications/TR2021-001}
      • }
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  • Videos