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.
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Researchers
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Awards
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AWARD MERL researchers presented 5 papers at the 2016 Optical Fiber Communication Conference (OFC), including one "Top Scored" paper Date: March 24, 2016
Awarded to: Toshiaki Koike-Akino, Keisuke Kojima, David S. Millar, Kieran Parsons, Tsuyoshi Yoshida, Takashi Sugihara
MERL Contacts: Toshiaki Koike-Akino; Keisuke Kojima; Kieran Parsons
Research Areas: Communications, Electronic and Photonic Devices, Signal ProcessingBrief- Five papers from the Optical Comms team were presented at the 2016 Optical Fiber Conference (OFC) held in Anaheim, USA in March 2016. The papers relate to enhanced modulation formats, constellation shaping, chromatic dispersion estimation, low complexity adaptive equalization and coding for coherent optical links. The top-scored paper studied optimal selection of coding and modulation sets to jointly maximize nonlinear tolerance and spectral efficiency.
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News & Events
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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 ProcessingBrief- 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.
- 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.
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EVENT MERL Virtual Open House 2020 Date & Time: Wednesday, December 9, 2020; 1:00-5:00PM EST
MERL Contacts: Elizabeth Phillips; Jeroen van Baar; Anthony Vetro
Location: Virtual
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 & AudioBrief- MERL will host a virtual open house on December 9, 2020. Live sessions will be held from 1-5pm EST, including an overview of recent activities by our research groups and a talk by Prof. Pierre Moulin of University of Illinois at Urbana-Champaign on adversarial machine learning. Registered attendees will also be able to browse our virtual booths at their convenience and connect with our research staff on engagement opportunities including internship, post-doc and research scientist openings, as well as visiting faculty positions.
Registration: https://mailchi.mp/merl/merl-virtual-open-house-2020
Schedule: https://www.merl.com/events/voh20
Current internship and employment openings:
https://www.merl.com/internship/openings
https://www.merl.com/employment/employment
Information about working at MERL:
https://www.merl.com/employment
- MERL will host a virtual open house on December 9, 2020. Live sessions will be held from 1-5pm EST, including an overview of recent activities by our research groups and a talk by Prof. Pierre Moulin of University of Illinois at Urbana-Champaign on adversarial machine learning. Registered attendees will also be able to browse our virtual booths at their convenience and connect with our research staff on engagement opportunities including internship, post-doc and research scientist openings, as well as visiting faculty positions.
See All News & Events for Electronic and Photonic Devices -
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Internships
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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.
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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
- "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}
- }
, - "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}
- }
, - "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}
- }
, - "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}
- }
, - "Spectrally sparse optical coherence tomography", Optics Express, DOI: 10.1364/OE.409539, Vol. 28, No. 25, pp. 37798-37810, October 2020.BibTeX TR2020-156 PDF
- @article{Atalar2020oct,
- author = {Atalar, Okan and Millar, David S. and Wang, Pu and Koike-Akino, Toshiaki and Kojima, Keisuke and Orlik, Philip V. and Parsons, Kieran},
- title = {Spectrally sparse optical coherence tomography},
- journal = {Optics Express},
- year = 2020,
- volume = 28,
- number = 25,
- pages = {37798--37810},
- month = oct,
- doi = {10.1364/OE.409539},
- url = {https://www.merl.com/publications/TR2020-156}
- }
, - "Inverse Design of Nanophotonic Devices using Deep Neural Networks", Asia Communications and Photonics Conference (ACP), September 2020, pp. Su1A.1.BibTeX TR2020-130 PDF Video
- @inproceedings{Kojima2020sep,
- author = {Kojima, Keisuke and Tang, Yingheng and Koike-Akino, Toshiaki and Wang, Ye and Jha, Devesh and Parsons, Kieran and TaherSima, Mohammad and Sang, Fengqiao and Klamkin, Jonathan and Qi, Minghao},
- title = {Inverse Design of Nanophotonic Devices using Deep Neural Networks},
- booktitle = {Asia Communications and Photonics Conference (ACP)},
- year = 2020,
- pages = {Su1A.1},
- month = sep,
- publisher = {Optical Society of America},
- isbn = {978-1-943580-82-8},
- url = {https://www.merl.com/publications/TR2020-130}
- }
, - "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}
- }
, - "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), DOI: 10.1109/IMS30576.2020.9223999, June 2020, pp. 277-280.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,
- pages = {277--280},
- month = jun,
- publisher = {IEEE},
- doi = {10.1109/IMS30576.2020.9223999},
- issn = {2576-7216},
- isbn = {978-1-7281-6815-9},
- url = {https://www.merl.com/publications/TR2020-080}
- }
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- "International Conference on Electron Device Meeting Report," Tech. Rep. TR2021-017, Mitsubishi Electric Research Laboratories, March 2021.
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Videos