Noise is one of the main factors affecting the performance index of weak signal detection devices, and the optimal filtering algorithm is an effective method to adaptively extract various useful weak signals from the white noise background. In order to improve the performance of single photon detector (especially the photon number resolution ability), one mainly focuses on the optimization of detector hardware such as the optimization of photosensitive materials and the technology of device fabrication. However, in this paper the performance of microwave kinetic Inductance detector (MKID) in the way of data processing is improved. Considering the fact that the template of light pulse signal in the optimal filtering algorithm is obtained by taking the average, we replace the noise model in the original optimal filtering algorithm with the white noise model and the whitening noise model. Then we process the photon response data that are detected by the MKID in an extremely low temperature environment. The results show that the energy resolution (one of the main performance indexes of single photon detector) of MKID is improved by about 15%, and we achieve an infrared single photon energy resolution of 0.26 eV. In this paper, the application and development trends of superconducting single photon detector are briefed. Then, how the MKID responds to weak coherent optical signal in low temperature environment, and the process of signal conversion, acquisition and output are explained in detail. According to the optimal filtering algorithm, we use different noise models to analyze the results of the signals detected by MKID. After that, we count the optimal amplitude multiple, perform the Gaussian fitting analysis on the statistical graph, and compare the energy resolution with the photon number resolution of the optimal filtering algorithm under different noise models. As a result, we find that under the white noise model, the optimal filtering algorithm is used to obtain the best result for MKID processing, and high energy resolution can be achieved.
Noise is one of the main factors affecting the performance index of weak signal detection devices, and the optimal filtering algorithm is an effective method to adaptively extract various useful weak signals from the white noise background. In order to improve the performance of single photon detector (especially the photon number resolution ability), one mainly focuses on the optimization of detector hardware such as the optimization of photosensitive materials and the technology of device fabrication. However, in this paper the performance of microwave kinetic Inductance detector (MKID) in the way of data processing is improved. Considering the fact that the template of light pulse signal in the optimal filtering algorithm is obtained by taking the average, we replace the noise model in the original optimal filtering algorithm with the white noise model and the whitening noise model. Then we process the photon response data that are detected by the MKID in an extremely low temperature environment. The results show that the energy resolution (one of the main performance indexes of single photon detector) of MKID is improved by about 15%, and we achieve an infrared single photon energy resolution of 0.26 eV. In this paper, the application and development trends of superconducting single photon detector are briefed. Then, how the MKID responds to weak coherent optical signal in low temperature environment, and the process of signal conversion, acquisition and output are explained in detail. According to the optimal filtering algorithm, we use different noise models to analyze the results of the signals detected by MKID. After that, we count the optimal amplitude multiple, perform the Gaussian fitting analysis on the statistical graph, and compare the energy resolution with the photon number resolution of the optimal filtering algorithm under different noise models. As a result, we find that under the white noise model, the optimal filtering algorithm is used to obtain the best result for MKID processing, and high energy resolution can be achieved.
Microwave Kinetic Inductance Detectors (MKIDs) are superconducting sensors primarily used in astrophysics and quantum measurements for their energy- and time-resolution of single-photon detection. However, their sensitivity and precision are limited by various noise sources. Conventional denoising approaches enhance the signal-to-noise ratio (SNR) but often blur signal features and are ineffective with non-stationary noise. This work explores the use of machine learning (ML) denoising techniques for MKID signals in steady state resonators and in photon-induced transients. Different denoising techniques are evaluated on a dataset of frequency-domain S21 measurements and time-domain phase pulses with illumination of 660 nm, 780 nm, and 1310 nm wavelengths. Traditional filtering approaches (moving average, median, Wiener, Savitzky-Golay, wavelet, Kalman, total variation denoising) were evaluated in comparison with ML models including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRUs). Due to absence of clean reference signals in experimental settings, a Noise2Noise training approach was used for self-supervised learning from noisy data. The models were assessed using mean squared error (MSE), signal-to-noise ratio (SNR), variation in peak depth and optical resolution metrics. Findings of this thesis show that machine learning models achieve substantially better denoising performance compared to conventional methods across all metrics. GRU models outperformed other models when denoising steady-state resonator signals, as they captured local features of the spectral response, and they also were the most effective at denoising transient photon signals due to their capacity to capture temporal dynamics. Conventional filters, while being computationally efficient, were found to distort the signal and were not as effective in retaining low-amplitude photon-induced features. In summary, this study demonstrates that machine learning denoising is a robust and versatile method for improving MKID signals without prior knowledge of noise properties. The results also demonstrate the opportunity to enhance superconducting detector readouts by combining data-driven approaches, leading to improved sensitivity and resolution for applications such as astrophysical imaging and quantum sensing.
As a newly developed pair-breaking superconducting detector, microwave kinetic inductance detectors are simple to integrate in the frequency domain and have already been used in astronomical detection and array imaging at the (sub)millimeter and optical wavelengths. For these applications, the dark noise level of kinetic inductance detector is one of the key performance indicators. Herein a noise power spectrum analysis method is introduced in detail, which can accurately and effectively analyze the noise spectrum of kinetic inductance detector in a wide frequency range. This method can well balance the noise spectrum resolution and variance performance, by taking the noise data at the resonance frequency with two sampling rates and setting the appropriate frequency resolutions for different frequency bands. This method is used to characterize and compare the noise of aluminum (Al) kinetic inductance detectors made from two different micromachining processes. We deposite a 25-nm-thick aluminum film on high-resistivity silicon substrate for one device, while place one silicon nitride (SiNx) film on the top and one on the bottom of the aluminum film for another device. It is found that the frequency noise of the device with two silicon nitride films is about 25% to 50% of the bare aluminum device. Using this double silicon nitride film fabrication technique, we further fabricate a few groups of lumped-element aluminum kinetic inductance detectors with various inductor and interdigitated capacitor designs. We investigate the noise properties of these devices at different microwave driven power and bath temperatures, and the experimental results show typical two-level system noise behaviors. Our work provides a standard method to characterize the noise power spectrum of kinetic inductor detector, and also paves the way to developing low-noise aluminum kinetic inductance detectors for terahertz imaging, photon-counting and energy-resolving applications.
Context. Microwave kinetic inductance detectors (MKIDs) are superconducting detectors that have the potential to contribute to the construction of over 10 000 pixel cameras required for mm and submm astronomical observations over the next decade. One of the issues related to its application is two-level-system (TLS) noise that creates a 1/ f term in the power spectrum density (PSD) of the detectors. A hybrid-type MKID has been proposed for TLS noise suppression without affecting the MKID performance. In this paper, we present a systematic method to model a hybrid-type MKID for mitigating TLS noise while achieving high sensitivity. Aims. Previous studies have shown that TLS noise in MKIDs depends on the sampling frequency, internal power, device geometry, and temperature. The strength of the electrical field distribution along the resonator line also affects the TLS noise properties. Based on this knowledge, we constructed a noise model of a hybrid-type MKID. Methods. Using this model, we designed MKIDs for the cosmic microwave background radiation experiment, GroundBIRD. We evaluated the 1/ f noise properties and NET of the fabricated devices using the on-site measurements. Based on the NET measurements, we can confirm that the white noise of fabricated detectors is limited by atmospheric photon noise. From the 1/ f noise measurements, we also confirm that it is possible to reach an atmospheric photon noise limit down to a low sampling frequency (i.e., of a few Hz) at the typical observation conditions from Teide observatory. Results. We confirm the design methodology we propose for hybrid-type MKID works well in optimizing the TLS model to the hybrid-type MKID, as evidenced by performance verification measurements carried out in the laboratory and on site.
Microwave Kinetic Inductance Detectors (MKIDs) are superconducting photon counting detectors that simultaneously measure the energy and arrival time of individual photons and are suitable for large-format arrays. The energy resolving power R = E/ΔE is the primary figure of merit for spectrophotometric applications. It is limited in practice by the assumptions underlying the Wiener optimal filter used to estimate photon energy: stationary noise, linear response, and energy independent pulse shapes, all of which MKIDs structurally violate. We present Venom (Very Efficient Neural Optimal-filter for MKIDs), a selective state space model based on the Mamba architecture that replaces the coordinate transform and optimal filter, operating directly on raw in-phase/quadrature timestream data, carrying 3,314 trainable parameters, and targeting deployment on the MKIDGen3 and future system-on-chip readout platforms. To deal with the small calibration datasets typical of MKID experiments, we train on synthetic pulses drawn from a streaming principal component analysis that samples photon energy continuously between calibration wavelengths. All numbers are reported on a stratified 15% held-out validation set, with R measured from a kernel density estimate. On an InHf bilayer resonator (ten wavelengths, 254-1310nm), Venom reaches a mean R of 26.4 versus 24.2 for the published per-wavelength optimal filter baseline, a 9% improvement from one model in place of ten per-laser filter templates. The R of 38.6 at 254 nm is the highest ever recorded for an ultraviolet-to-near-infrared MKID suitable for use in a dense array. On a PtSi resonator (five wavelengths, 808-1310nm), where the published analysis used one shared 920nm template, Venom matches the optimal filter, with a mean R of 9.1 versus 8.8 at the three interior wavelengths.
On the Efficiency η and the Homogeneity of TiN Kinetic Inductance Detectors Produced through Atomic Layer Deposition
Microwave Kinetic Inductance Detectors (MKIDs) belong to non-equilibrium superconducting devices. It is a new promising device due to high quantum efficiency, low theoretical noise limit, multiplex frequency domain, photon-number-resolving, and energy resolving. In the case of MKIDs, a change in the kinetic inductance is produced by the breaking of Cooper pairs, modifying the electrodynamic response of the superconductor. The production process of an MKID consists of depositing a superconducting thin film on an insulating substrate and applying standard lithographic patterning techniques to produce a resonator structure. When an MKID is produced via sputtering, film nonuniformity is observed, producing instability, low yield, frequency collisions, and low energy resolution. We exploring film uniformity in films produced with Atomic Layer Deposition (ALD) and through ALD archive high values in the energy resolution. The uniformity of ALD is evaluated via measurement of the critical temperature of 4 MKIDs. Two MKIDs belong to the first fabrication process and the other two to another. The results obtained are: ΔTc/max(σT1,σT2) is 0.45 for A1-B1 and 0.21 for A2-B2. The difference in the Tc for MKIDs of the same wafer is around 5.1 mK and from different wafers, the maximum difference is 66 mk with a minimum of 48 mK. However, with the high uniformity archived, we obtained a poor response of the MKIDs when a photon is absorbed. As a consequence of the low response, we examined in the literature about the process of photon absorption. In this evaluation, we were focusing on the down-conversion efficiency as a possible cause for the degraded energy resolution. In this process, one part of the initial energy of the photon is lost, transferred to the creation of quasiparticles and phonons. The phonons that do not possess enough energy to break a Cooper pair are lost from the system. This loss in energy is accounted for by the eff iciency η which has a constant value reported as 0.57 (! from simulation). Due to the efficiency had been reported always as constant. We proceed to evaluate its value through a new method. The process consists of measuring the gain of the system via the photon transfer curve and thermal calibration. The efficiency obtained with this new method is of 0.008 which has a lower value in comparison with the simulation. The methods to obtain the critical temperature, as well as the new method to evaluate the efficiency of an MKID will be shown in this work.
Abstract We report on the experimental investigation of optical coupling for superconducting microresonators known as microwave kinetic inductance detectors (MKIDs) in the visible and near-infrared bands. MKIDs are photon-counting, time and energy-resolving detectors that still suffer from a poor quantum efficiency. To improve this efficiency, we propose to add a superconducting reflective layer below the absorbing part of the detector separated by a transparent Al2O3 layer with a quarter-wavelength thickness optimized around a single wavelength λ = 405 nm. We have first fabricated samples patterned from stoichiometric TiN ( T c ∼ 4 K), one with the full optical stack, one without for reference and one with a partial optical stack in order to characterize the noise influence of each layer individually. We observe that the full optical stack geometry has the most impact on the resonator’s noise and quality factors. A second design was fabricated to characterize the optical response to short pulses of the optical stack and we show from both the frequential noise and optical response that a strong signature of TLS is still present in the optical stack sample. We have finally obtained single-photon response with the optical stack using a more sensitive tri-layer TiN/Ti/TiN absorber ( T c ∼ 1.3 K) for which a maximum energy resolving power of R = E / Δ E ∼ 1.3 was achieved using 405 nm laser pulses at 225 mK. The quality factors of both the reference and optical stack samples are similar but the frequency noise is still a tenfold higher for the optical stack sample which degrades the energy-resolving power of the detector.