Optical absorption of silicon nitride membranes at 1064 nm and at 1550 nm

Steinlechner, J.; Krüger, Christoph; Martin, I. W.; Bell, A. S.; Hough, J.; Kaufer, H.; Rowan, S.; Schnabel, R.; Steinlechner, S.

doi:10.1103/physrevd.96.022007

Cited by 24 publications

(27 citation statements)

References 32 publications

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“…SiN has very low mechanical loss at low temperatures [32,33] and a low optical absorption compared to a-Si at 1.55 μm [39], which we find to reduce further when moving to 2 μm. We show that an a-Si=SiN coating at 20 K would have an optical absorption of 27 ppm at 2 μm.…”

mentioning

confidence: 58%

“…−5 ) at 1.55 μm [38]. For the work presented in this Letter, we have measured the absorption of SiN at 2 μm resulting in k ¼ 4.3 × 10 −6 .…”

mentioning

confidence: 97%

“…The use of a-Si as a high-index material opens new possibilities for low-index partner materials with n < n Si , e.g., silicon nitride (SiN) [37,38]. SiN has very low mechanical loss at low temperatures [32,33] and a low optical absorption compared to a-Si at 1.55 μm [39], which we find to reduce further when moving to 2 μm.…”

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confidence: 98%

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Silicon-Based Optical Mirror Coatings for Ultrahigh Precision Metrology and Sensing

et al. 2018

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Thermal noise of highly reflective mirror coatings is a major limit to the sensitivity of many precision laser experiments with strict requirements such as low optical absorption. Here, we investigate amorphous silicon and silicon nitride as an alternative to the currently used combination of coating materials, silica, and tantala. We demonstrate an improvement by a factor of ≈55 with respect to the lowest so far reported optical absorption of amorphous silicon at near-infrared wavelengths. This reduction was achieved via a combination of heat treatment, final operation at low temperature, and a wavelength of 2 μm instead of the more commonly used 1550 nm. Our silicon-based coating offers a factor of 12 thermal noise reduction compared to the performance possible with silica and tantala at 20 K. In gravitational-wave detectors, a noise reduction by a factor of 12 corresponds to an increase in the average detection rate by three orders of magnitude (≈12 3 ).

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mentioning

confidence: 58%

“…−5 ) at 1.55 μm [38]. For the work presented in this Letter, we have measured the absorption of SiN at 2 μm resulting in k ¼ 4.3 × 10 −6 .…”

mentioning

confidence: 97%

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Silicon-Based Optical Mirror Coatings for Ultrahigh Precision Metrology and Sensing

et al. 2018

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“…PCI has the advantage that it measures only the absorption while, e.g., cavity round-trip loss measurements include surface scattering, which makes the result less accurate. More details about the measurements, including some special considerations for the thin membranes, in which etalon effects can occur, are explained in Steinlechner et al (2017).…”

Section: Absorption × Thermo-optic Coefficient As a Function Of Tempementioning

confidence: 99%

“…In addition, the very high refractive index (n Si = 3.5 at 1,550 nm) would reduce the numbers of layers required to achieve high reflectivity as the reflectivity per pair of high and low refractive layers increases with the contrast between the refractive indices. However, the optical absorption of aSi is too high for application in GW detectors at the envisioned wavelength of 1,550 nm (Steinlechner et al, 2014(Steinlechner et al, , 2017 and there has been research over the past years to reduce the absorption and to find ways to exploit the good mechanical properties while keeping the absorption low (Steinlechner et al, 2015;Yam et al, 2015;). An aSi/SiO 2 multilayer coating would be dominated by the mechanical loss of the SiO 2 layers, but Si 3 N 4 would be a suitable low-index material with a similarly low mechanical loss as aSi.…”

Section: Introductionmentioning

confidence: 99%

Effect of Stress and Temperature on the Optical Properties of Silicon Nitride Membranes at 1,550 nm

et al. 2018

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Future gravitational-wave detectors operated at cryogenic temperatures are expected to be limited by thermal noise of the highly reflective mirror coatings. Silicon nitride is an interesting material for such coatings as it shows very low mechanical loss, a property related to low thermal noise, which is known to further decrease under stress. Low optical absorption is also required to maintain the low mirror temperature. Here, we investigate the effect of stress on the optical properties at 1,550 nm of silicon nitride membranes attached to a silicon frame. Our approach includes the measurement of the thermal expansion coefficient and the thermal conductivity of the membranes. The membrane and frame temperatures are varied, and translated into a change in stress using finite element modeling. The resulting product of the optical absorption and thermo-optic coefficient (dn/dT) is measured using photothermal common-path interferometry.

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Spiderweb Nanomechanical Resonators via Bayesian Optimization: Inspired by Nature and Guided by Machine Learning

et al. 2021

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temperature conditions where thermomechanical noise can dominate. The degree of mechanical isolation is characterized by a resonator's mechanical quality factor, Q m . Typically, Q m is defined as the ratio of energy stored in a resonator over the energy dissipated over one cycle of oscillation. Inversely, mechanical quality factors can indicate the dissipation of mechanical noise into a resonator from ambient environments. For mechanical sensors, a resonator's isolation from ambient thermal noise can greatly enhance their ability to detect ultrasmall forces, pressures, positions, masses, velocities, and accelerations. For quantum technologies, mechanical quality factor dictates the average number of coherent oscillations a nanomechanical resonator (in the quantum regime) can undergo before one phonon of thermal noise enters the resonator and causes decoherence of its quantum properties. [1] From microchip sensing to quantum networks, cryogenics are conventionally required to counteract thermal noise but enabling these burgeoning technologies to operate in ambient temperatures would have a significant impact on their widespread use.In room-temperature environments, on-chip mechanical resonators with state-of-the-art quality factors have mostly consisted of high-aspect-ratio suspended nanostructures From ultrasensitive detectors of fundamental forces to quantum networks and sensors, mechanical resonators are enabling next-generation technologies to operate in room-temperature environments. Currently, silicon nitride nanoresonators stand as a leading microchip platform in these advances by allowing for mechanical resonators whose motion is remarkably isolated from ambient thermal noise. However, to date, human intuition has remained the driving force behind design processes. Here, inspired by nature and guided by machine learning, a spiderweb nanomechanical resonator is developed that exhibits vibration modes, which are isolated from ambient thermal environments via a novel "torsional soft-clamping" mechanism discovered by the data-driven optimization algorithm. This bioinspired resonator is then fabricated, experimentally confirming a new paradigm in mechanics with quality factors above 1 billion in room-temperature environments. In contrast to other state-of-the-art resonators, this milestone is achieved with a compact design that does not require sub-micrometer lithographic features or complex phononic bandgaps, making it significantly easier and cheaper to manufacture at large scales. These results demonstrate the ability of machine learning to work in tandem with human intuition to augment creative possibilities and uncover new strategies in computing and nanotechnology.

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Optical absorption of silicon nitride membranes at 1064 nm and at 1550 nm

Cited by 24 publications

References 32 publications

Silicon-Based Optical Mirror Coatings for Ultrahigh Precision Metrology and Sensing

Silicon-Based Optical Mirror Coatings for Ultrahigh Precision Metrology and Sensing

Effect of Stress and Temperature on the Optical Properties of Silicon Nitride Membranes at 1,550 nm

Spiderweb Nanomechanical Resonators via Bayesian Optimization: Inspired by Nature and Guided by Machine Learning

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