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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

SCA Test Report: H4RG-21225 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm. This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test,the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗
NASA NTRS2020

SCA Test Report: H4RG-21317 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm.This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗
NASA NTRS2020

SCA Test Report: H4RG-21319 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 µm. This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗
NASA NTRS2020

Evaluation Issues for a Flight Deck Interface; CAST SE-210 Output 2: Report 4 of 6

This report is part of a series of reports that addresses flight deck design and evaluation, written as a response to loss of control accidents. In particular, this activity is directed at failures in airplane state awareness, in which the pilot loses awareness of the airplane’s energy state or attitude and enters an upset condition. Another report in this series of reports speaks directly to flight deck evaluation methods and metrics for the types of attention and awareness issues that were revealed from the airplane state awareness events. In this report, we describe a wide range of flight deck evaluation issues tied to flight crew performance. The objectives are to establish a framework for thinking about how the flight deck interface should support the performance of the flight crew, and to aid the Federal Aviation Administration (FAA) in identifying relevant human performance issues during the evaluation/certification process. Issues are broken out into sections that cover physical ergonomics, design for usability, data integration and display content, attention and task management, flight crew problem solving, and flight crew teaming. For each issue, we recommend specific ways that the flight deck interface should support the flight crew. For each, we also identify existing 14 CFR Part 25 rules and guidance that are relevant to the issue. This allows the FAA to determine what current regulatory materials can support them in raising the issue with the applicant.

commercial aviation↗
NASA NTRS2021

SCA Test Report H4RG-21645 Roman Space Telescope

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm. This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCAATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗
NASA NTRS2021

SCA Test Report H4RG-21813: Roman Space Telescope

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm.This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-P ROC-09220_WFIRST-SCA-ATP_-.docx.A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary ( Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗
NASA NTRS2021

SCA Test Report H4RG-21814: Roman Space Telescope

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm.This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-P ROC-09220_WFIRST-SCA-ATP_-.docx.A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary ( Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗
NASA NTRS2021

SCA Test Report H4RG-21815: Roman Space Telescope

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm.This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx.A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗
NASA NTRSDate not supplied

The Influence of Self-Reported Distractions on the Psychomotor Vigilance Task (PVT) in Laboratory and Field Environments

Introduction: The Psychomotor Vigilance Task (PVT) is a measure of vigilant attention that is commonly used in laboratory environments to assess the neurobehavioral impact of sleep loss and circadian misalignment. The PVT has been increasing in popularity for use in field environments; however, the potential for distraction is higher in the field compared to the lab. It is unclear how distractions experienced by individuals taking the PVT in the real world may influence reaction time metrics. We investigated the influence of self-reported distraction on PVT outcomes across laboratory and field environments. Methods: We examined PVT data from five studies including short (n=36 participants, 3799 PVTs) and long-haul (n=75 participants, 3283 PVTs) airline personnel, control center personnel (n=5 participants, 96 PVTs), and healthy individuals who participated in a study involving at-home and laboratory assessments (n=12 participants, 486 and 310 PVTs). Individuals in all of the studies were asked to complete the five-minute NASA PVT at least three times daily. Participants were asked to indicate the number of distractions they experienced immediately after each PVT. Mean PVT reaction time (RT) and number of distractions were computed for each study and overall. Results: Participants reported more distractions in field environments compared to the lab (short-haul=1.29 +/- 1.48, long-haul=0.66 +/- 1.07, control-center=1.20 +/- 1.37, at-home=0.86 +/- 1.36, laboratory=0.46 +/- 1.07) Across all studies, we found that PVT RT slowed as self-reported distractions increased (all studies combined: 0 distractions=PVT RT 274.4ms; 1=284.6ms; 2=302.9ms; 3=321.2ms; >4=409.3ms). These findings were similar for healthy participants completing PVTs at home (0 distractions=287.1ms; 1=309.9ms; 2=328.3ms; 3=369.8ms; >4=385.1ms) but were less consistent during in-lab assessments (0 distractions=278.7ms; 1=316.2ms; 2=396.2ms; 3=370.4ms; >4=354.4ms). These findings were similar for other PVT outcomes. Conclusion: Participants reported more distractions in field environments compared to the laboratory. Our findings suggest that the number of distractions that individuals report experiencing while taking a PVT increases the reaction time registered by the device. Researchers should collect information about distractions during the PVT and should be aware that distractions may influence the recorded PVT reaction time.

behavior↗
NASA NTRS2022

A validation of satellite derived cyanobacteria detections with state reported events and recreation advisories across U.S. lakes

Cyanobacteria harmful algal blooms (cyanoHABs) negatively affect ecological, human, and animal health. Traditional methods of validating satellite algorithms with data from water samples are often inhibited by the expense of quantifying cyanobacteria indicators in the field and the lack of public data. However, state recreation advisories and other recorded events of cyanoHAB occurrence reported by local authorities can serve as an independent and publicly available dataset for validation. State recreation advisories were defined as a period delimited by a start and end date where a warning was issued due to detections of cyanoHABs over a state’s risk threshold. State reported events were defined as any event that was documented with a single date related to cyanoHABs. This study examined the presence-absence agreement between 160 state reported cyanoHAB advisories and 1,343 events and cyanobacteria biomass estimated by a satellite algorithm called the Cyanobacteria Index (CI(cyano)). The true positive rate of agreement with state recreation advisories was 69% and 60% with state reported events. CI(cyano) detected a reduction or absence in cyanobacteria after 76% of the recreation advisories ended. CI(cyano) was used to quantify the magnitude, spatial extent, and temporal frequency of cyanoHABs; each of these three metrics were greater (r > 0.2) during state recreation advisories compared to non-advisory times with effect sizes ranging from small to large. This is the first study to quantitatively evaluate satellite algorithm performance for detecting cyanoHABs with state reported events and advisories and supports informed management decisions with satellite technologies that complement traditional field observations.

remote sensing↗
NASA NTRS2022

Natural Language Processing Techniques for Intelligent Knowledge Management of Safety Reports

Safety, failure, and incident reports are common artifacts across various domains, including aviation and wildfire response. These reports are often mandatory to submit, resulting in the culmination of large repositories of text-based documents. Simultaneously, these reports and corresponding repositories are often only manually analyzed and queried by users via out-of-date search engines. As a consequence, we have been developing the Manager for Intelligent Knowledge Access (MIKA) toolkit, which uses natural language processing to improve information access and reuse. In this presentation, we discuss natural language processing techniques for knowledge discovery and apply these methods to a repository of aerial wildfire mishap reports. Two methods are used for knowledge discovery: topic modeling and named-entity recognition. We use topic modeling to identify hazards and perform a trend analysis to produce a data-driven risk matrix. A custom named-entity recognition model, build from fine tuning a pre-trained language model, is used to identify failure modes, failure causes, failure effects, control processes, and recommendations to aid in failure modes and effects analysis (FMEA). Throughout the presentation, we discuss and apply natural language processing techniques to better leverage the vast amount of information contained in report repositories.

Machine learning↗
NASA NTRS2023

Commercial Smallsat Data Acquisition Program On-ramp #2 Airbus U.S. Synthetic Aperture Radar (SAR) Evaluation Report

In 2017, NASA’s Earth Science Division (ESD) launched the Private-Sector Small Constellation Satellite Data Product Pilot, now referred to as the Commercial Smallsat Data Acquisition (CSDA) program. The objective of CSDA is to identify, evaluate, and acquire commercial remote sensing data that support NASA’s Earth science research and application activities. The Pilot successfully concluded in early 2020, when CSDA transitioned into a sustained program with on-ramping opportunities for new vendors as the industry emerges with new candidates and capabilities. In October 2019, a Request for Information (RFI) seeking capability statements from parties interested in providing data from spaceborne platforms was released for the CSDA on-ramp #2 evaluations. To be responsive to the RFI, the commercial satellite constellations had to consist of three or more operating spacecraft actively collecting data in a non-geostationary orbit with full latitudinal coverage and be U.S. companies. Two vendors responded to the RFI and were evaluated by a committee composed of NASA ESD leadership, program managers, and scientists. Both vendors satisfied the RFI requirements and were asked to respond to a Request for Proposal (RFP). After review of the proposals, NASA entered into a Blanket Purchase Agreement (BPA) with Airbus Defense and Space GEO, Inc. (Airbus) U.S. in September 2021 and with BlackSky Geospatial Solutions, Inc. (BlackSky) in November 2021. In this report, CSDA provides an evaluation of the usefulness of data provided by the Airbus U.S. Synthetic Aperture Radar (SAR) satellite constellation, consisting of TerraSAR-X (launched in 2007), TanDEM-X (launched in 2010), and PAZ (launched in 2018), for advancing NASA’s Earth system science research and applications. The evaluation of the BlackSky commercial data will be provided in a separate report. To conduct the Airbus evaluation, NASA’s ESD augmented 13 existing research projects that could potentially benefit from, and had the expertise to evaluate, the commercial data being considered for longer-term purchase. Investigators from NASA’s Research and Analysis Program science focus areas and from NASA’s Applied Sciences Program elements participated in the evaluation. A summary of the research areas evaluated by the Principal Investigator (PI) teams is presented in Figure 3. CSDA also funded a dedicated activity to evaluate the satellite data quality (calibration and geolocation) independently by assessing the accuracy of data from Airbus. Evaluation activities were carried out by the selected PIs from December 7, 2022, to December 7, 2023. Delivery of datasets requested by the researchers began in January 2023. The vendors were evaluated on the accessibility of data, accuracy and completeness of metadata, and promptness and quality of user support services. Datasets purchased during the evaluation have been archived by NASA and will be made available to current and future government-funded researchers in accordance with the End User License Agreement (EULA). This synthesis report distills and integrates the findings of research reports commissioned by NASA for the Airbus evaluation. This report also includes recommendations that inform the way ahead for the program. The scientific results from the evaluations demonstrated that the commercial data from Airbus were able to advance NASA research and applications. However, the PIs encountered limitations that diminished the usefulness of the data due to the amount of effort that was required to access, preprocess, and analyze these data. One significant issue encountered was the limited spatial and temporal coverage of the data in the Airbus archive that could be used to conduct time series analyses or assessments over large spatial scales. Overall, however, the utility and the quality of the evaluated data outweighed the difficulties encountered, and NASA has concluded that the Airbus SAR data would complement NASA’s existing Earth observation capabilities and Airbus U.S. would qualify to participate in the sustained phase of the program.

Batuhan Osmanoglu↗
NASA NTRS2023

Evidence Report: Risk of Ineffective or Toxic Medication During Long-Duration Exploration Spaceflight

The scope of this report is limited to evaluating drug stability with respect to spaceflight. The effects of spaceflight on drug pharmacokinetics (PK) or drug pharmacodynamics (PD) are not addressed in this report: these topics will be reviewed in future reports. This is the first report of the available evidence pertaining to the chemical and physical stability of drugs in the context of drug impurity, drug repackaging, and exposure to ionizing space radiation. Previous studies are discussed within this report and, where possible, the data are interpreted and contextualized with regards to drug stability.

John F. Reichard↗
NASA NTRS2024

2023 Land Use Control Inspections Report

2023 Land Use Control (LUC) Inspections Report for John F. Kennedy Space Center (KSC), Florida. This letter report documents the quarterly LUC inspections the KSC Remediation Group conducts at Remediation Sites. No LUC findings occurred in 2023. A Corrective Action to address arsenic-impacted soil at the KSC Child Development Center was performed in July 2023. This letter report provides a summary and documents the field activities for that Corrective Action. Copies of the formal LUC Implementation Plans (LUCIPs) for 71 Remediation Sites at KSC are presented in this letter report. For the Remediation Sites that do not have formal LUCIPs, the report documents that informal LUCs that the Remediation Group has implemented at these sites.

Christopher Adkison↗
NASA NTRS2024

Per- and Polyfluoroalkyl Substances Confirmatory Sampling Report, South Repeater Building (SWMU 121), Kennedy Space Center, Florida

This Per- and Polyfluoroalkyl Substances (PFAS) Confirmatory Sampling (CS) Report was prepared by AECOM Technical Services, Inc. (AECOM) for the National Aeronautics and Space Administration (NASA) under Contract 80KSC019D0010, Task Order 80KSC021F0096. The purpose of this report is to document CS activities at the South Repeater Building, Solid Waste Management Unit (SWMU) 121, formerly known as the Area 3 Repeater Building (A3RB). A Phase I SWMU Assessment and Confirmatory Sampling Report (NASA 2019) and a Phase II and III SWMU Assessment and Confirmatory Sampling Report (NASA 2022b) were previously submitted detailing Center-Wide assessment activities performed at the John F. Kennedy Space Center (KSC) in Florida that identified 33 locations of concern and 19 areas of potential concern at KSC associated with the storage, use, or release of materials containing PFAS. A review of the analytical results from investigation derived waste samples collected in 2020 from the South Repeater Building area indicated concentrations of perfluorooctanesulfonic acid (PFOS) were above Florida Department of Environmental Protection (FDEP) provisional Groundwater Cleanup Target Levels of 70 nanograms per liter (ng/L) at a concentration of 1,750 ng/L. Following this review, the KSC Fire Chief was contacted; the Chief recalled fighting a brush fire in 1998 in the area, during which an unknown volume of aqueous film-forming foam was used to extinguish the fire. The Phase II and Phase III SWMU Assessment Report recommended this area proceed to CS. Historical results in this area are provided in Appendix A . PFAS CS activities were contracted at the South Repeater Building. Initial CS activities were conducted in accordance with the PFAS Assessment and Mitigation Work Plan (AECOM 2022), which was submitted to the NASA Remediation Project Management team and accepted by the team on November 1 and 8, 2021. The objectives of the PFAS CS were to: - - Confirm and begin to assess the extent of PFAS affected media in the South Repeater Building area. - - Identify locations for groundwater, soil, surface water, and soil core sampling. - - Qualitatively characterize the migration potential of released PFAS to environmental media (soil, groundwater, and surface water). The following activities were completed to meet the PFAS CS objectives: - - Installation of monitoring wells via direct push technology (DPT) and rotosonic techniques - - Groundwater, soil, and surface water sampling - - Collection of soil cores to evaluate site lithology - - Development of initial PFAS plume characterization

PFAS↗
NASA NTRSDate not supplied

Identifying Human Errors and Error Mechanisms From Accident Reports Using Large Language Models

Emerging operational concepts for aviation hinge on novel paradigms for human machine interaction. Critical to their safe operation is early consideration of human error into the design process. Existing methods for consideration of human error require significant expert input, which is challenging both in early design and in novel systems for which there is little existing safety expertise. In this research, we propose a methodology for identifying human error, error producing factors, and mechanisms in early design from historical incident reports. Additionally, we hypothesize that cross-domain sharing of lessons learned can aid with early design human considerations in circumstances where data is not relevant or incomplete. This is addressed by identifying causes of human error in aviation and railway domains through applying state-of-the art natural language processing techniques to historical incident reports. Using this method, it is possible to extract extensive reports on human error from past incidents. Using the proposed approach, we identify nine human errors from railway reports and fourteen from aviation reports, with three errors common to both domains. There is at least one error producing conditions for each human error while a majority of the errors have more than one error mechanism. We also found that a majority of the human errors, error producing factors, and error mechanisms (even if they are not common between the domains) can be used to inform safe operations across domains as long as the errors are not domain specific and are interpreted and contextualized using engineering judgement.

Human Errors↗
NASA NTRS2024

Evidence Report: Risk of Renal Stone Formation

Kidney stone formation and passage has the potential to greatly impact mission success and crewmember health, especially for long-duration missions. Alterations in hydration state (relative dehydration), spaceflight-induced changes in urine biochemistry (urine super-saturation), and bone metabolism (increased calcium excretion) during exposure to microgravity may increase the risk of kidney stone formation. There are possible countermeasures and treatments available that are used terrestrially that may then be applied to spaceflight. Directed Acyclic Graphs (DAGs) are used throughout this document to communicate spaceflight conditions that may lead to renal stone formation, the countermeasures that may be used to prevent their formation, and possible treatment modalities. The DAGs are sorted by strength of evidence, according to Table 6. Additionally, for the full Renal Stone Evidence Report Content: Directed Acyclic Graphs and Evidence Report, please see Appendix A, Expanded Directed Acyclic Graphs (DAGs) and Evidence. Additionally, a proposed Concept of Operations for the Prevention, Diagnosis, and Treatment of Renal Stones for a Mars Mission was created in conjunction with and to complement this Evidence Report update. Please see Appendix B, Proposed Expanded Concept of Operations for the Prevention, Diagnosis and Treatment of Renal Stones for Mars Missions, for the full report. Areas in the following Evidence Report will be cross-linked to scenarios from the Concept of Operations.

Emily Stratton↗
NASA NTRS2020

Sensor Chip Assembly (SCA) Test Report

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm. This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 90 K (1.0V bias voltage) and 95 K (0.5V bias voltage) are also reported. For all cases, the frame time used is 2.764 seconds. Reference pixel correction was applied to every raw frame.

WFIRST SCA TEST REPORT↗