Investigation of random telegraph signal in two junction layouts of proton irradiated CMOS SPADs

Capua, F. Di; Campajola, M.; Fiore, D.; Gasparini, Leonardo; Sarnelli, E.; Aloisio, A.

doi:10.1038/s41598-021-87962-w

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…The Mie channel, which analyzes narrow bandwidth aerosol returns, incorporates a Fizeau interferometer (FIZ) and is based on the fringe imaging technique (McKay, 1998). To measure the broad bandwidth return from molecules, the double-edge technique is used in the Rayleigh channel, which is made up of two sequential Fabry-Perot interferometers (FPI) (Chanin et al, 1989;Flesia and Korb, 1999).…”

Section: Measurement Principlementioning

confidence: 99%

Characterization of dark current signal measurements of the ACCDs used on board the Aeolus satellite

et al. 2021

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Abstract. Even just shortly after the successful launch of the European Space Agency satellite Aeolus in August 2018, it turned out that dark current signal anomalies of single pixels (so-called “hot pixels”) on the accumulation charge-coupled devices (ACCDs) of the Aeolus detectors detrimentally impact the quality of the aerosol and wind products, potentially leading to wind errors of up to several meters per second. This paper provides a detailed characterization of the hot pixels that occurred during the first 1.5 years in orbit. The hot pixels are classified according to their characteristics to discuss their impact on wind measurements. Furthermore, mitigation approaches for the wind retrieval are presented and potential root causes for hot pixel occurrence are discussed. The analysis of the dark current signal anomalies reveals a large variety of anomalies ranging from pixels with random telegraph signal (RTS)-like characteristics to pixels with sporadic shifts in the median dark current signal. Moreover, the results indicate that the number of hot pixels almost linearly increased during the observing period between 2 September 2018 and 20 May 2020 with 6 % of the ACCD pixels affected in total at the end of the period leading to 9.5 % at the end of the mission lifetime. This work introduces dedicated instrument calibration modes and ground processors, which allowed for a correction shortly after a hot pixel occurrence. The achieved performance with this approach avoids risky adjustments to the in-flight hardware operation. It is demonstrated that the success of the correction scheme varies depending on the characteristics of each hot pixel itself. With the herein presented categorization, it is shown that multi-level RTS pixels with high fluctuation are the biggest challenge for the hot pixel correction scheme. Despite a detailed analysis in this framework, no conclusion could be drawn about the root cause of the hot pixel issue.

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Section: Measurement Principlementioning

confidence: 99%

Characterization of dark current signal measurements of the ACCDs used on board the Aeolus satellite

et al. 2021

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“…For example, in a submicron-sized metal-oxide-semiconductor field-effect transistor (MOSFET), if there is a trap to capture or emit a single charge carrier, device currents in the transport channel exhibit arbitrary patterns of two-level RTSs over time. Similar RTS phenomena are discerned in superconducting qubits 14 , 15 , single photon avalanche diodes 16 , ultrasensitive biosensors 17 , and single-cell activities in ion channels 18 . Real-time dynamics in quantum electrical devices also reveal rapid jumping sequences 19 , 20 .…”

Section: Introductionmentioning

confidence: 56%

Deep neural network analysis models for complex random telegraph signals

Robitaille

Yang

Wang

et al. 2023

Sci Rep

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Time-fluctuating signals are ubiquitous and diverse in many physical, chemical, and biological systems, among which random telegraph signals (RTSs) refer to a series of instantaneous switching events between two discrete levels from single-particle movements. A reliable RTS analysis is a crucial prerequisite to identify underlying mechanisms related to device performance and sensitivity. When numerous levels are involved, complex patterns of multilevel RTSs occur and make their quantitative analysis exponentially difficult, hereby systematic approaches are often elusive. In this work, we present a three-step analysis protocol via progressive knowledge-transfer, where the outputs of the early step are passed onto a subsequent step. Especially, to quantify complex RTSs, we resort to three deep neural network architectures whose trained models can process raw temporal data directly. We furthermore demonstrate the model accuracy extensively with a large dataset of different RTS types in terms of additional background noise types and amplitude size. Our protocol offers structured schemes to extract the parameter values of complex RTSs as imperative information with which researchers can draw meaningful and relevant interpretations and inferences of given devices and systems.

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“…For example, in a submicron-sized MOSFET, if there is a trap to capture or emit a single charge carrier, device currents in the transport channel exhibit arbitrary patterns of two-level RTSs over time. Similar RTS phenomena are discerned in superconducting qubits [14], single photon avalanche diodes [15], ultrasensitive biosensors [16], and single-cell activities in ion channels [17]. Real-time dynamics in quantum electrical devices also reveal rapid jumping sequences [18], [19].…”

mentioning

confidence: 63%

Extensive Study of Multiple Deep Neural Networks for Complex Random Telegraph Signals

Robitaille

Yang

Wang

et al. 2022

Preprint

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Time-fluctuating signals are ubiquitous and diverse in many physical, chemical, and biological systems, among which random telegraph signals (RTSs) refer to a series of instantaneous switching events between two discrete levels from single-particle movements. Reliable RTS analyses are crucial prerequisite to identify underlying mechanisms related to performance sensitivity. When numerous levels partake, complex patterns of multilevel RTSs occur, making their quantitative analysis exponentially difficult, hereby systematic approaches are found elusive. Here, we present a three-step analysis protocol via progressive knowledge-transfer, where the outputs of early step are passed onto a subsequent step. Especially, to quantify complex RTSs, we build three deep neural network architectures that can process temporal data well and demonstrate the model accuracy extensively with a large dataset of different RTS types affected by controlling background noise size. Our protocol offers structured schemes to quantify complex RTSs from which meaningful interpretation and inference can ensue.

show abstract

Investigation of random telegraph signal in two junction layouts of proton irradiated CMOS SPADs

Cited by 8 publications

References 41 publications

Characterization of dark current signal measurements of the ACCDs used on board the Aeolus satellite

Characterization of dark current signal measurements of the ACCDs used on board the Aeolus satellite

Deep neural network analysis models for complex random telegraph signals

Extensive Study of Multiple Deep Neural Networks for Complex Random Telegraph Signals

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