• Chinese Optics Letters
  • Vol. 22, Issue 3, 032501 (2024)
James Garofolo and Ben Wu*
Author Affiliations
  • Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ 08028, USA
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    DOI: 10.3788/COL202422.032501 Cite this Article Set citation alerts
    James Garofolo, Ben Wu. Photonic analog signal processing and neuromorphic computing [Invited][J]. Chinese Optics Letters, 2024, 22(3): 032501 Copy Citation Text show less

    Abstract

    In this review paper, we discuss the properties and applications of photonic computing and analog signal processing. Photonic computational circuits have large operation bandwidth, low power consumption, and fine frequency control, enabling a wide range of application-specific computational techniques that are impossible to implement using traditional electrical and digital hardware alone. These advantages are illustrated in the elegant implementation of optical steganography, the real-time blind separation of signals in the same bandwidth, and the efficient acceleration of artificial neural network inference. The working principles and use of photonic circuits for analog signal processing and neuromorphic computing are reviewed and notable demonstrated applications are highlighted.
    [x1x2]=[a11a12a21a22][s1s2],

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    X=AS,

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    S=A1X.

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    cond(A):=A*A1,

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    A:=maxj(i=1n|ai,j|).

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    aijaiki,jk.

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    w(θ)=[cos(θ),sin(θ)].

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    m2t(θ)=q1+q2cos[2(θθ0)],

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    w=UΣU1[cos(ϕ),sin(ϕ)],

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    m4t(ϕ)=p1+p2cos[2(ϕϕ0)]+p3cos[4(ϕϕ0)].

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    A1=VUΣU1.

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    SNRstealth=I2σthermal2+σshot2+σASEASE2,

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    I2=(2RSspΔυopt)2,

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    σthermal2=(4kBT/RL)FnΔf,

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    σshot2=2qR(2SspΔυopt)Δf,

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    σASEASE2=4R22Ssp2ΔυoptΔf,

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    SNRstealth=ΔυoptΔf((4kBT/RL)Fn(2RSsp)2Δυopt+qRSsp+1).

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    SNRstealth=ΔυoptΔf.

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    TThru=(ar)22r2acos(ϕ)+r2(ar)22r2acos(ϕ)+(r2a)2,

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    ϕ=4π2dneffλ,

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    TDrop=(1r2)2a(ar)22r2acos(ϕ)+(r2a)2.

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    James Garofolo, Ben Wu. Photonic analog signal processing and neuromorphic computing [Invited][J]. Chinese Optics Letters, 2024, 22(3): 032501
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