Automatic Resonance Alignment of High-Order Microring Filters

Mak, Jason C. C.; Sacher, Wesley D.; Xue, Tianyuan; Mikkelsen, Jared C.; Yong, Zheng; Poon, Joyce K. S.

doi:10.1109/jqe.2015.2479939

Cited by 76 publications

(36 citation statements)

References 37 publications

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“…The inset illustrates the integration of photoconductive heaters into each of the rings. The rings, starting from the ring coupled to the input-port, are denoted as R 1 through R 14 . The ring-ring and ring-bus waveguide couplings were chosen for a maximally flat drop-port response (see Supplementary section 1).…”

Section: Tuning Of 14-ring Crowmentioning

confidence: 99%

“…Prior-art for controlling ring resonators have depended on numerous photodetectors (PDs) for monitoring the rings' resonance conditions and on separate thermo/electro-optic phase shifters for tuning the rings [11][12][13][14][15][16]. However, these extra components often need additional processing steps (e.g., Ge depositions [14,17] or Si+ implantations [18] for PD), increase the number of electrical inputs/outputs to the system, and occupy significantly large on-chip real estate [11,[13][14][15]. Therefore, a low-cost single element which can be placed inside the resonator for monitoring and tuning its resonance would be a true enabler for controlling large-scale systems.…”

Section: Introductionmentioning

confidence: 99%

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Photoconductive heaters enable control of large-scale silicon photonic ring resonator circuits

Jayatilleka

Shoman

Chrostowski

et al. 2019

Optica

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A multitude of large-scale silicon photonic systems based on ring resonators have been envisioned for applications ranging from biomedical sensing to quantum computing and machine learning. Yet, due to the lack of a scalable solution for controlling ring resonators, practical demonstrations have been limited to systems with only a few rings. Here, we demonstrate that large systems can be controlled only by using doped waveguide elements inside their rings whilst preserving their area and cost. We measure the large photoconductive changes of the waveguides for monitoring rings' resonance conditions across high-dynamic ranges and leverage their thermo-optic effects for tuning. This allows us to control ring resonators without requiring additional components, complex tuning algorithms, or additional electrical I/Os. We demonstrate automatic resonance alignment of 31 rings of a 16 × 16 switch and of a 14-ring coupled resonator optical waveguide (CROW), making them the largest, yet most compact, automatically controlled silicon ring resonator circuits to date.

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Section: Tuning Of 14-ring Crowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Photoconductive heaters enable control of large-scale silicon photonic ring resonator circuits

Jayatilleka

Shoman

Chrostowski

et al. 2019

Optica

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“…Sensitivity to fabrication variations, thermal fluctuations, and thermal cross-talk has made MRR control an important topic for WDM demultiplexers [153], highorder filters [154], modulators [155], and delay lines [156]. Commonly, the goal of MRR control is to track a particular point in the resonance relative to the signal carrier wavelength, such as its center or maximum slope point.…”

Section: Controlling Photonic Weight Banksmentioning

confidence: 99%

Principles of Neuromorphic Photonics

Shastri¹,

Tait²,

Lima³

et al. 2018

Unconventional Computing

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GLOSSARYBenchmark A standardized task that can be performed by disparate computing approaches, used to assess their relative processing merit in specific cases. Bifurcation A qualitative change in behavior of a dynamical system in response to parameter variation. Examples include cusp (from monostable to bistable), Hopf (from stable to oscillating), transcritical (exchange of stability between two steady states). Brain-inspired computing (a.k.a. neuro-inspired computing) A biologically inspired approach to build processors, devices, and computing models for applications including adaptive control, machine learning, and cognitive radio. Similarities with biological signal processing include architectural, such as distributed, representational, such as analog or spiking, or algorithmic, such as adaptation. Broadcast and Weight A multi-wavelength analog networking protocol in which multiple all photonic neuron outputs are multiplexed and distributed to all neuron inputs. Weights are reconfigured by tunable spectral filters. Excitability A far-from-equilibrium nonlinear dynamical mechanism underlying all-or-none responses to small perturbations. Fan-in The number of inputs to a neuron. Layered network A network topology consisting of a series of sets (i.e., layers) of neurons. The neurons in each set project their outputs only to neurons in the subsequent layer. Most commonly used type of network used for machine learning. Metric A quantity assessing performance of a device in reference to a specific computing approach. Microring weight bank A silicon photonic implementation of a reconfigurable spectral filter capable of independently setting transmission at multiple carrier wavelengths. Modulation The act of representing an abstract variable in a physical quantity, such as photon rate (i.e., optical power), free carrier density (i.e., optical gain), carrier drift (i.e., current). Electrooptic modulators are devices that convert from an electrical signal to the power envelope of an optical signal. Recently, the scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of neuromorphic photonics (Fig. 1). This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.tion processing, describe the photonic neural-network approaches being developed by our lab and others, and photonic integrated circuit (PIC) platforms, which have recently undergone rapid growth. PICs are becoming a

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“…Sensitivity to fabrication variations, thermal fluctuations, and thermal crosstalk have made MRR control an important topic for WDM demultiplexers [105], high-order filters [106], modulators [107], and delay lines [108]. Commonly, the goal of MRR control is to track a particular point in the resonance relative to the signal carrier wavelength, such as its center or maximum slope point.…”

Section: Controlling Photonic Weight Banksmentioning

confidence: 99%

Progress in neuromorphic photonics

et al. 2017

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As society's appetite for information continues to grow, so does our need to process this information with increasing speed and versatility. Many believe that the one-size-fits-all solution of digital electronics is becoming a limiting factor in certain areas such as data links, cognitive radio, and ultrafast control. Analog photonic devices have found relatively simple signal processing niches where electronics can no longer provide sufficient speed and reconfigurability. Recently, the landscape for commercially manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. By bridging the mathematical prowess of artificial neural networks to the underlying physics of optoelectronic devices, neuromorphic photonics could breach new domains of information processing demanding significant complexity, low cost, and unmatched speed. In this article, we review the progress in neuromorphic photonics, focusing on photonic integrated devices. The challenges and design rules for optoelectronic instantiation of artificial neurons are presented. The proposed photonic architecture revolves around the processing network node composed of two parts: a nonlinear element and a network interface. We then survey excitable lasers in the recent literature as candidates for the nonlinear node and microring-resonator weight banks as the network interface. Finally, we compare metrics between neuromorphic electronics and neuromorphic photonics and discuss potential applications.

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Automatic Resonance Alignment of High-Order Microring Filters

Cited by 76 publications

References 37 publications

Photoconductive heaters enable control of large-scale silicon photonic ring resonator circuits

Photoconductive heaters enable control of large-scale silicon photonic ring resonator circuits

Principles of Neuromorphic Photonics

Progress in neuromorphic photonics

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