scholarly journals 4.2 K sensitivity-tunable radio frequency reflectometry of a physically defined p-channel silicon quantum dot

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sinan Bugu ◽  
Shimpei Nishiyama ◽  
Kimihiko Kato ◽  
Yongxun Liu ◽  
Shigenori Murakami ◽  
...  
Keyword(s):  
Quantum Dots ◽  
Quantum Dot ◽  
Radio Frequency ◽  
Liquid Helium ◽  
Signal To Noise Ratio ◽  
Matching Condition ◽  
Silicon On Insulator ◽  
Signal To Noise ◽  
Silicon Quantum Dot ◽  
Optimal Signal

AbstractWe demonstrate the measurement of p-channel silicon-on-insulator quantum dots at liquid helium temperatures by using a radio frequency (rf) reflectometry circuit comprising of two independently tunable GaAs varactors. This arrangement allows observing Coulomb diamonds at 4.2 K under nearly best matching condition and optimal signal-to-noise ratio. We also discuss the rf leakage induced by the presence of the large top gate in MOS nanostructures and its consequence on the efficiency of rf-reflectometry. These results open the way to fast and sensitive readout in multi-gate architectures, including multi qubit platforms.

scholarly journals Geometric Approach to Analytic Marginalisation of the Likelihood Ratio for Continuous Gravitational Wave Searches

Universe ◽  
2021 ◽  
Vol 7 (6) ◽  
pp. 174
Author(s):  
Karl Wette
Keyword(s):  
Gravitational Wave ◽  
Likelihood Ratio ◽  
Signal To Noise Ratio ◽  
Geometric Approach ◽  
The Other ◽  
Efficient Computation ◽  
Likelihood Ratios ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Optimal Signal

The likelihood ratio for a continuous gravitational wave signal is viewed geometrically as a function of the orientation of two vectors; one representing the optimal signal-to-noise ratio, and the other representing the maximised likelihood ratio or F-statistic. Analytic marginalisation over the angle between the vectors yields a marginalised likelihood ratio, which is a function of the F-statistic. Further analytic marginalisation over the optimal signal-to-noise ratio is explored using different choices of prior. Monte-Carlo simulations show that the marginalised likelihood ratios had identical detection power to the F-statistic. This approach demonstrates a route to viewing the F-statistic in a Bayesian context, while retaining the advantages of its efficient computation.

Development of a Philips cryo-TEM provided with a liquid helium cooled tilt stage and a vacuum transfer system

1998 ◽  
Vol 4 (S2) ◽  
pp. 400-401
Author(s):  
R. Wagner ◽  
A.F. de Jong ◽  
A.G. Koster ◽  
R. Morrison ◽  
F. Tothill ◽  
...  
Keyword(s):  
High Resolution ◽  
Liquid Helium ◽  
Signal To Noise Ratio ◽  
Transfer System ◽  
Helium Temperature ◽  
Single Particles ◽  
Signal To Noise ◽  
Specimen Holder ◽  
2D Crystals ◽  
Beam Damage

In order to reduce beam damage, biological TEM specimens are often observed at temperatures close to the boiling point of liquid nitrogen (77 K). Recently, encouraging results on single particles as well as on 2D crystals have appeared, derived from images taken near liquid helium temperature (4 K), in dedicated TEMs. At these temperatures the high resolution frequencies are much better preserved, increasing the allowable dose and thus the signal to noise ratio.4 Here we present the design of a new dedicated Philips He-TEM which combines the full functionality of a CM300 TWIN with a vacuum transfer system and a liquid helium cooled specimen holder.A schematic overview of the Cryo-TEM is shown in figure 1. The key differences compared to a standard CM microscope are: 1) The tip of the specimen rod is cooled below 10 K and the rod itself cannot be taken out of the goniometer (CompuStage). 2) The specimen enters the column on the opposite side.

scholarly journals Optimal signal-to-noise ratio for silicon nanowire biochemical sensors

2011 ◽  
Vol 98 (26) ◽  
pp. 264107 ◽  
Author(s):  
Nitin K. Rajan ◽  
David A. Routenberg ◽  
Mark A. Reed
Keyword(s):  
Signal To Noise Ratio ◽  
Silicon Nanowire ◽  
Signal To Noise ◽  
Biochemical Sensors ◽  
Noise Ratio ◽  
Optimal Signal

Plasma engineering of silicon quantum dots and their properties through energy deposition and chemistry

2016 ◽  
Vol 18 (37) ◽  
pp. 25837-25851 ◽  
Author(s):  
Bibhuti Bhusan Sahu ◽  
Yongyi Yin ◽  
Sven Gauter ◽  
Jeon Geon Han ◽  
Holger Kersten
Keyword(s):  
Quantum Dots ◽  
Quantum Dot ◽  
Energy Deposition ◽  
Plasma Chemistry ◽  
Silicon Quantum Dots ◽  
Silicon Quantum Dot ◽  
Deposition Energy

The authors growth and microstructure of a silicon quantum dot film by tailoring the plasma chemistry and deposition energy are studied.

scholarly journals Receiver phase alignment using fitted SVD derived sensitivities from routine prescans

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256700
Author(s):  
Olivia W. Stanley ◽  
Ravi S. Menon ◽  
L. Martyn Klassen
Keyword(s):  
Singular Value Decomposition ◽  
Radio Frequency ◽  
Signal To Noise Ratio ◽  
Singular Value ◽  
7 Tesla ◽  
Signal To Noise ◽  
Phase Signal ◽  
Noise Ratio ◽  
Value Decomposition ◽  
Svd Method

Magnetic resonance imaging radio frequency arrays are composed of multiple receive coils that have their signals combined to form an image. Combination requires an estimate of the radio frequency coil sensitivities to align signal phases and prevent destructive interference. At lower fields this can be accomplished using a uniform physical reference coil. However, at higher fields, uniform volume coils are lacking and, when available, suffer from regions of low receive sensitivity that result in poor sensitivity estimation and combination. Several approaches exist that do not require a physical reference coil but require manual intervention, specific prescans, or must be completed post-acquisition. This makes these methods impractical for large multi-volume datasets such as those collected for novel types of functional MRI or quantitative susceptibility mapping, where magnitude and phase are important. This pilot study proposes a fitted SVD method which utilizes existing combination methods to create a phase sensitive combination method targeted at large multi-volume datasets. This method uses any multi-image prescan to calculate the relative receive sensitivities using voxel-wise singular value decomposition. These relative sensitivities are fitted to the solid harmonics using an iterative least squares fitting algorithm. Fits of the relative sensitivities are used to align the phases of the receive coils and improve combination in subsequent acquisitions during the imaging session. This method is compared against existing approaches in the human brain at 7 Tesla by examining the combined data for the presence of singularities and changes in phase signal-to-noise ratio. Two additional applications of the method are also explored, using the fitted SVD method in an asymmetrical coil and in a case with subject motion. The fitted SVD method produces singularity-free images and recovers between 95–100% of the phase signal-to-noise ratio depending on the prescan data resolution. Using solid harmonic fitting to interpolate singular value decomposition derived receive sensitivities from existing prescans allows the fitted SVD method to be used on all acquisitions within a session without increasing exam duration. Our fitted SVD method is able to combine imaging datasets accurately without supervision during online reconstruction.

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