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Kaori WARABI Rai KOU Shinichi TANABE Tai TSUCHIZAWA Satoru SUZUKI Hiroki HIBINO Hirochika NAKAJIMA Koji YAMADA
Graphene is attracting attention in electrical and optical research fields recently. We measured the optical absorption characteristics and polarization dependence of single-layer graphene (SLG) on sub-micrometer Si waveguide. The results for graphene lengths ranging from 2.5 to 200 $mu$ m reveal that the optical absorption by graphene is 0.09 dB/$mu$ m with the TE mode and 0.05 dB/$mu$ m with the TM mode. The absorption in the TE mode is 1.8 times higher than that in the TM mode. An optical spectrum, theoretical analysis and Raman spectrum indicate that surface-plasmon polaritons in graphene support TM mode light propagation.
Seiichi ITABASHI Hidetaka NISHI Tai TSUCHIZAWA Toshifumi WATANABE Hiroyuki SHINOJIMA Rai KOU Koji YAMADA
Monolithic integration of various kinds of optical components on a silicon wafer is the key to making silicon (Si) photonics practical technology. Applying silicon photonics to telecommunications further requires low insertion loss and polarization independence. We propose an integration concept for telecommunications based on Si and related materials and demonstrate monolithic integration of passive and dynamic functional components. This article shows the great potential of Si photonics technology for telecommunications.
Guangwei CONG Noritsugu YAMAMOTO Takashi INOUE Yuriko MAEGAMI Morifumi OHNO Shota KITA Rai KOU Shu NAMIKI Koji YAMADA
Wide deployment of artificial intelligence (AI) is inducing exponentially growing energy consumption. Traditional digital platforms are becoming difficult to fulfill such ever-growing demands on energy efficiency as well as computing latency, which necessitates the development of high efficiency analog hardware platforms for AI. Recently, optical and electrooptic hybrid computing is reactivated as a promising analog hardware alternative because it can accelerate the information processing in an energy-efficient way. Integrated photonic circuits offer such an analog hardware solution for implementing photonic AI and machine learning. For this purpose, we proposed a photonic analog of support vector machine and experimentally demonstrated low-latency and low-energy classification computing, which evidences the latency and energy advantages of optical analog computing over traditional digital computing. We also proposed an electrooptic Hopfield network for classifying and recognizing time-series data. This paper will review our work on implementing classification computing and Hopfield network by leveraging silicon photonic circuits.
Rai KOU Sungbong PARK Tai TSUCHIZAWA Hiroshi FUKUDA Hidetaka NISHI Hiroyuki SHINOJIMA Koji YAMADA
We demonstrate phase demodulation of 10-Gbps DPSK signals using a silicon micro-ring resonator with a radius of 10 µm and with various coupling gaps for light of ∼1550 nm in wavelength. Influence of the Q factors and transmissions of the resonators on the response speed and power balance of the two output ports is discussed. Furthermore, temperature sensitivity on resonance peak was measured and we discuss its effect on practical demodulation application.