• Corpus ID: 116161417

Roadmap on Atto-Joule per Bit Modulators

@article{Sorger2017RoadmapOA,
  title={Roadmap on Atto-Joule per Bit Modulators},
  author={Volker J. Sorger and Rubab Amin and Jacob B. Khurgin and Zhizhen Ma and Hamed Dalir and Sikandar Khan},
  journal={arXiv: Applied Physics},
  year={2017}
}

• V. Sorger, R. Amin, +3 authors Sikandar Khan
• Published 15 September 2017
• Physics
• arXiv: Applied Physics

Electrooptic modulation performs the conversion between the electrical and optical domain with applications in data communication for optical interconnects, but also for novel optical compute algorithms such as providing nonlinearity at the output stage of optical perceptrons in neuromorphic analogue optical computing. Since the clock frequency for photonics on chip has a power overhead sweet slot around 10s of GHz, ultrafast modulation may only be required in long distance communication, but not for short onchip links. Here we show a roadmap towards atto Joule per bit efficient modulators on chip as well as some experimental demonstrations of novel plasmon modulators with sub 1fJ per bit efficiencies. We then discuss the first experimental demonstration of a photon plasmon-hybrid Graphene-based electroabsorption modulator on silicon. The device shows a sub 1V steep switching enabled by near ideal electrostatics delivering a high 0.05dB per V um performance requiring only 110 aJ per bit. Improving on this design, we discuss a plasmonic slot based Graphene modulator design, where the polarization of the plasmonic mode matches with Graphenes inplane dimension. Here a push pull dual gating scheme enables 2dB per V um efficient modulation allowing the device to be just 770 nm short for 3dB small signal modulation. This in turn allows for a device-enabled two orders of magnitude improvement of electrical optical co integrated network on chips over electronic only architectures. The latter opens technological opportunities in in cognitive computing, dynamic data-driven applications system, and optical analogue compute engines to include neuromorphic photonic computing.