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   Mitsubishi Electric Corporation and MERL Press Release Describes New 5G GaN Power Amplifier Technology
      Date: July 14, 2020
      Where: Tokyo, Japan
      MERL Contact: Rui Ma
      Research Areas: Communications, Electronic and Photonic Devices
      Brief
      • Mitsubishi Electric Corporation announced today its developement of a new technology to realize a gallium nitride (GaN) power amplifier module for 5G base-stations that offers a combination of compact (6mm by 10mm) footprint and high power-efficiency, the latter exceeding an unprecedented rating of 43%.

        MERL and Mitsubishi Electric researchers collaborated to develop high density mounting technology and matching circuit that uses a minimum number of chip components to achieve efficient, wide-band power amplification in the 3.4-3.8GHz bands used for 5G communication.

        Please see the link below for the full Mitsubishi Electric press release text. Technical details of the new module will be presented at the IEEE International Microwave Symposium this coming August.
    •  
    •  TALK   GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning
      Date & Time: Tuesday, July 14, 2020; 11:00 AM
      Speaker: Hanrui Wang, MIT
      MERL Host: Rui Ma
      Research Areas: Electronic and Photonic Devices, Machine Learning
      Brief
      • Automatic transistor sizing is a challenging problem in circuit design due to the large design space, complex performance trade-offs, and fast technological advancements. Although there has been plenty of work on transistor sizing targeting on one circuit, limited research has been done on transferring the knowledge from one circuit to another to reduce the re-design overhead. In this work, we present GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies. Moreover, inspired by the simple fact that circuit is a graph, we learn on the circuit topology representation with graph convolutional neural networks (GCN). The GCN-RL agent extracts features of the topology graph whose vertices are transistors, edges are wires. Our learning-based optimization consistently achieves the highest Figures of Merit (FoM) on four different circuits compared with conventional black-box optimization methods (Bayesian Optimization, Evolutionary Algorithms), random search, and human expert designs. Experiments on transfer learning between five technology nodes and two circuit topologies demonstrate that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer. Our transferable optimization method makes transistor sizing and design porting more effective and efficient. The work is accepted to DAC 2020.
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  • Internships

    • MD1370: Machine Learning based DPD for Power Amplifier

      MERL is looking for a talented intern to work on the next generation Digital-predistortion algorithms for power amplifier linearization such as 5G. The development of a DPD system involves aspects of signal processing and statistical algorithm design, RF components and instrumentation, digital hardware and software. It is therefore both a challenging and intellectually rewarding experience. This will involve MATLAB coding, interfacing to test equipment such as power sources, signal generators and analyzers and construction and calibration of RF component assemblies. The ideal candidate should have knowledge and experience in adaptive signal processing, machine learning, and radio communication. Good practical laboratory skills are needed. RF semiconductor devices and circuit knowledge is a plus. Duration is 3 to 6 months.

    • MD1441: Advanced Phased Array Transceiver

      MERL is looking for a highly motivated, and qualified individual to join our internship program of advanced phased array research. The ideal candidate should be a senior Ph.D. student with rich experience in beam forming technologies. Knowledge of wireless communication, transceiver architecture, and digital signal processing, FPGA and/or Matlab programming skills are required. RF circuits knowledge will be a plus. Duration is 3-6 months with a flexible start date.


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  • Recent Publications

    •  Skvortcov, P., Phillips, I., Forysiak, W., Koike-Akino, T., Kojima, K., Parsons, K., Millar, D.S., "Nonlinearity Tolerant LUT-based Probabilistic Shaping for Extended-Reach Single-Span Links", IEEE Photonics Technology Letters, DOI: 10.1109/LPT.2020.3006737, Vol. 32, No. 16, pp. 967-970, July 2020.
      BibTeX TR2020-107 PDF
      • @article{Skvortcov2020jul,
      • author = {Skvortcov, Pavel and Phillips, Ian and Forysiak, Wladek and Koike-Akino, Toshiaki and Kojima, Keisuke and Parsons, Kieran and Millar, David S.},
      • title = {Nonlinearity Tolerant LUT-based Probabilistic Shaping for Extended-Reach Single-Span Links},
      • journal = {IEEE Photonics Technology Letters},
      • year = 2020,
      • volume = 32,
      • number = 16,
      • pages = {967--970},
      • month = jul,
      • doi = {10.1109/LPT.2020.3006737},
      • issn = {1941-0174},
      • url = {https://www.merl.com/publications/TR2020-107}
      • }
    •  Komatsuszaki, Y., Ma, R., Sakata, S., Nakatani, K., Shinjo, S., "A Dual-Mode Bias Circuit Enabled GaN Doherty Amplifier Operating in 0.85-2.05GHz and 2.4-4.2GHz", IEEE International Microwave Symposium (IMS), June 2020.
      BibTeX TR2020-080 PDF
      • @inproceedings{Komatsuszaki2020jun,
      • author = {Komatsuszaki, Yuji and Ma, Rui and Sakata, Shuichi and Nakatani, Keigo and Shinjo, Shintaro},
      • title = {A Dual-Mode Bias Circuit Enabled GaN Doherty Amplifier Operating in 0.85-2.05GHz and 2.4-4.2GHz},
      • booktitle = {IEEE International Microwave Symposium (IMS)},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-080}
      • }
    •  Sakata, S., Kato, K., Teranishi, E., Sugitani, T., Ma, R., Chuang, K., Wu, Y., Fukunaga, K., Komatsuszaki, Y., Kenichi, H., Yamanaka, K., Shinjo, S., "A Fully-Integrated GaN Doherty Power Amplifier Module with a Compact Frequency-Dependent Compensation Circuit for 5G massive MIMO Base Stations", IEEE International Microwave Symposium (IMS), June 2020.
      BibTeX TR2020-077 PDF
      • @inproceedings{Sakata2020jun,
      • author = {Sakata, Shuichi and Kato, Katsuya and Teranishi, Eri and Sugitani, Takumi and Ma, Rui and Chuang, Kevin and Wu, Yuchen and Fukunaga, Kei and Komatsuszaki, Yuji and Kenichi, Horiguchi and Yamanaka, Koji and Shinjo, Shintaro},
      • title = {A Fully-Integrated GaN Doherty Power Amplifier Module with a Compact Frequency-Dependent Compensation Circuit for 5G massive MIMO Base Stations},
      • booktitle = {IEEE International Microwave Symposium (IMS)},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-077}
      • }
    •  Sravan Kumar, P., Ma, R., "Design Considerations and FPGA Implementation of a Wideband All-Digital Transmit Beamformer with 50% Fractional Bandwidth", IEEE International Microwave Symposium (IMS), June 2020.
      BibTeX TR2020-078 PDF
      • @inproceedings{SravanKumar2020jun,
      • author = {Sravan Kumar, Pulipati and Ma, Rui},
      • title = {Design Considerations and FPGA Implementation of a Wideband All-Digital Transmit Beamformer with 50% Fractional Bandwidth},
      • booktitle = {IEEE International Microwave Symposium (IMS)},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-078}
      • }
    •  Fehenberger, T., Millar, D.S., Koike-Akino, T., Kojima, K., Parsons, K., Griesser, H., "Huffman-coded Sphere Shaping and Distribution Matching Algorithms via Lookup Tables", IEEE Journal of Lightwave Technology, DOI: 10.1109/JLT.2020.2987210, Vol. 38, No. 10, pp. 2825-2833, April 2020.
      BibTeX TR2020-051 PDF
      • @article{Fehenberger2020apr2,
      • author = {Fehenberger, Tobias and Millar, David S. and Koike-Akino, Toshiaki and Kojima, Keisuke and Parsons, Kieran and Griesser, Helmut},
      • title = {Huffman-coded Sphere Shaping and Distribution Matching Algorithms via Lookup Tables},
      • journal = {IEEE Journal of Lightwave Technology},
      • year = 2020,
      • volume = 38,
      • number = 10,
      • pages = {2825--2833},
      • month = apr,
      • doi = {10.1109/JLT.2020.2987210},
      • issn = {1558-2213},
      • url = {https://www.merl.com/publications/TR2020-051}
      • }
    •  Zhang, S., Zhang, S., Wang, B., Habetler, T., "Deep Learning Algorithms for Bearing Fault Diagnostics – A Comprehensive Review", IEEE Access, DOI: 10.1109/ACCESS.2020.2972859, Vol. 8, pp. 29857-29881, March 2020.
      BibTeX TR2020-034 PDF
      • @article{Zhang2020mar,
      • author = {Zhang, Shen and Zhang, Shibo and Wang, Bingnan and Habetler, Thomas},
      • title = {Deep Learning Algorithms for Bearing Fault Diagnostics – A Comprehensive Review},
      • journal = {IEEE Access},
      • year = 2020,
      • volume = 8,
      • pages = {29857--29881},
      • month = mar,
      • doi = {10.1109/ACCESS.2020.2972859},
      • issn = {2169-3536},
      • url = {https://www.merl.com/publications/TR2020-034}
      • }
    •  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}
      • }
    •  Tang, Y., Kojima, K., Koike-Akino, T., Wang, Y., Wu, P., TaherSima, M., Jha, D., Parsons, K., Qi, M., "Generative Deep Learning Model for a Multi-level NanoOptic Broadband Power Splitter", Optical Fiber Communication Conference and Exposition (OFC), DOI: 10.1364/OFC.2020.Th1A.1, March 2020, pp. Th1A.1.
      BibTeX TR2020-025 PDF
      • @inproceedings{Tang2020mar,
      • author = {Tang, Yingheng and Kojima, Keisuke and Koike-Akino, Toshiaki and Wang, Ye and Wu, Pengxiang and TaherSima, Mohammad and Jha, Devesh and Parsons, Kieran and Qi, Minghao},
      • title = {Generative Deep Learning Model for a Multi-level NanoOptic Broadband Power Splitter},
      • booktitle = {Optical Fiber Communication Conference and Exposition (OFC)},
      • year = 2020,
      • pages = {Th1A.1},
      • month = mar,
      • publisher = {OSA},
      • doi = {10.1364/OFC.2020.Th1A.1},
      • isbn = {978-1-943580-71-2},
      • url = {https://www.merl.com/publications/TR2020-025}
      • }
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