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- W4225012172 abstract "Abstract The retention-of-state functionality provided by memories is fundamental to any Turing machine and neural network, hence is critical for any information system today. While emerging optical machine learning accelerators and photonic neuromorphic computing paradigms provide promising signal processing and computing performance, the lack of a photon-photon force in the universe makes storing optical information challenging. Fortunately, phase change materials provide such a missing memristive nonvolatile function via their reconfigurable crystalline structure and allow for rapid optical READ paradigms. However, demonstrations of photonic memory are limited by high optical loss, low state-cyclability, and rely on cumbersome non-CMOS like optical programmability. To overcome all three shortcomings and unlock the full potential of optical information storage and access, here we introduce a photonic random-access memory featuring vanishing low optical loss, demonstrate more than half a million switching cycles, a 100x improvement over state-of-art, and realize electrical programmability on-chip. The exceedingly low optical absorption (0.0015 dB/μm) is achieved via a novel broadband transparent phase change material, Ge2Sb2Se5 integrated atop a nanophotonic waveguide of a silicon chip. We show a highly efficient signal modulation (0.2 dB/μm) achieved by realizing a newly designed paired micro-heaters along both sides of waveguide, which allows for electronic-standard programmability of these photonic memories. When interrogated by an optical beam, they offer picosecond-short memory READ latency. Furthermore, we demonstrate a partial amorphization scheme realizing multi-state memory levels on a single heater enhancing footprint efficiency. Lastly, we verify the energy and switching speed and show how each trades-off with heater-to-waveguide proximity and signal strength, respectively. Such as CMOS-near electronically programmed and optical read photonic random-access memory with low-optical loss yet efficient programmability can become a crucial building block for network edge AI system of the looming industry 4.0 era." @default.
- W4225012172 created "2022-04-29" @default.
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- W4225012172 date "2022-04-26" @default.
- W4225012172 modified "2023-10-14" @default.
- W4225012172 title "Electrical Programmable Low-loss high cyclable Nonvolatile Photonic Random-Access Memory" @default.
- W4225012172 doi "https://doi.org/10.21203/rs.3.rs-1527814/v1" @default.
- W4225012172 hasPublicationYear "2022" @default.
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