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Dataset results
144 results for “Semiconductor”
Comparison Between Myocardial Tomoscintigraphies Using a Semiconductor Camera or a Conventional Camera
ClinicalTrials.gov study NCT02861235. IPD Sharing: NO. Countries: 1. Publications: 0.
Stress and Rest Myocardial Tomoscintigraphies Using Mono- or Double-isotope Protocol With a Semiconductor Camera
ClinicalTrials.gov study NCT02869126. IPD Sharing: NO. Countries: 1. Publications: 0.
The Effect of Semiconductor Knee Sleeves on Pain in Osteoarthritis of the Knee.
ClinicalTrials.gov study NCT05255835. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Semiconductor Heat Extraction Cooling
ClinicalTrials.gov study NCT04915859. IPD Sharing: NO. Countries: 1. Publications: 0.
Interest of the SPECT-CT Scan SUVspect in the New Generation of Gamma-cameras With Semiconductor
ClinicalTrials.gov study NCT03414021. IPD Sharing: NO. Countries: 1. Publications: 0.
Semiconductor Embedded Socks for Ankle Sprains
ClinicalTrials.gov study NCT07025733. IPD Sharing: NO. Countries: 1. Publications: 0.
Placebo Controlled Trial of a Titanium Dioxide Semiconductor Toothbrush on Mild-to-moderate Gum Disease
ClinicalTrials.gov study NCT00167466. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Clinical Investigation of the Effects of Semiconductor Embedded Therapeutic Garments on Cancer-related Cognitive Impairment in Breast and Gynecological Cancer Patients
ClinicalTrials.gov study NCT07173101. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of a Semiconductor Camera for the DaTSCAN™ Exam
ClinicalTrials.gov study NCT03980418. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Mapping replication domains using the Ion Torrent semiconductor sequencing platform [BrdU-IP]
GEO Series GSE115847. Mus musculus. 2 samples. Type: Other.
Chip-based soliton microcomb module using a hybrid semiconductor laser
<p><strong>Data for the paper Chip-based soliton microcomb module using a hybrid semiconductor lase</strong></p>
The influence of inhomogeneities and defects on novel Quantum Well and Quantum Dot based Infrared -emitting Semiconductor Lasers
<p><strong>Semiconductor Science and Technology article.</strong></p> <p>In this paper we discuss the development of new semiconductor materials and approaches to overcome the fundamental limitations of well established (Al,In)GaAs/InP and InGaAsP/InP infrared-emitting lasers. We consider three approaches; dilute-nitride InGaAsN-based structures; InAs-based quantum dot/dash structures and the most recently emerging dilute-bismide (GaAsBi, InGaAsBi), all of which may be grown on either GaAs or InP substrates. These material systems provide a range of possibilities for band engineering and strain control, thereby giving new routes to improve device efficiency, overcoming existing limitations of device performance and to develop range of new cost-efficient devices with improved characteristics. However, all of these approaches have common difficulties related to establishing optimised growth conditions to produce high quality material for device fabrication. Particularly, in this paper we compare and contrast the effects of inhomogeneous carrier distribution in these systems and discuss the influence of this on the physical properties of lasers developed using these approaches.</p> <p>The authors dedicate this paper to the memory of Professor Naci Balkan (Essex) and Professor Jeff Hosea (Surrey) both of whom made significant contributions to the development of new semiconductor materials including the dilute nitrides and bismides as discussed in the paper. We are also pleased to acknowledge the many collaborators who have contributed to the development of the materials and devices considered in this work, including; Tyndall National Institute (Ireland), Philipps-Universität Marburg (Germany), University of Sheffield (UK), Universität Würzburg (Germany), Fujitsu Laboratories Ltd (Japan), Infineon Technologies AG (Germany) and the Center for Physical Sciences and Technology (Lithuania). We gratefully acknowledge funding from EPSRC (grants EP/H005587/1, EP/G064725/1, EP/N021037/1) and the EU-FP7 “BIANCHO” project (FP7-257974).</p>
Bose-Einstein Condensation of Light in a Semiconductor Quantum Well Microcavity
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Effect of Semiconductor Embedded Wearable Sleeve on Treatment of Primary Dysmenorrhea Symptoms
ClinicalTrials.gov study NCT07176780. IPD Sharing: Not stated. Countries: 0. Publications: 0.
New Sensing Application to Diagnose Power Semiconductor Aging in Actuator Power Drive Systems
This paper presents a new sensing application to diagnose power semiconductor aging in power drive systems. It has been shown previously that device parasitic characteristics change during the aging process which results in detectable changes in their frequency response. This change is manifested in the current signal at very high frequencies. Therefore, using a wideband AC current sensor, high frequency components of the current can be acquired, providing a way to detect device aging.
Identification of Spatial Fault Patterns in Semiconductor Wafers
Abstract The semiconductor industry is constantly searching for new ways to increase the rate of both process development and yield learning. As more data is being collected and stored throughout the chip manufacturing process, it has become increasingly more difficult to analyze yield signals using traditional statistical methods. Most of the serious yield issues manifest themselves as non-random electrical failure maps. Our semi-supervised fault detection framework has elements of Spatial Signature Analysis (SSA) to capture yield signals for very large datasets without losing the critical details typically involved with summarization techniques. It includes signature detection, de-noising, clustering, and purification that allow one to create a true spatial response metric of the yield issue. Once this has been accomplished, one can load process data to join with the spatial response and invoke customized rule induction algorithms that generate a set of hypotheses - likely process causes for a specific spatial target response. The framework has been successfully used at Intel and represents an example of the growing influence of modern statistical learning in the semiconductor industry. **Speaker:** **Dr. Eugene Tuv, Intel** Dr. Eugene Tuv is a Senior Staff Research Scientist in the Logic Technology Department at Intel. His research interests include supervised and unsupervised non-parametric machine learning with massive heterogeneous data. Prior to Intel he worked as a research scientist in the Institute of Nuclear Research, Ukrainian Academy of Science. He holds postgraduate degrees in Mathematics and Applied Statistics.
Accelerated Aging System for Prognostics of Power Semiconductor Devices
Prognostics is an engineering discipline that focuses on estimation of the health state of a component and the prediction of its remaining useful life (RUL) before failure. Health state estimation is based on actual conditions and it is fundamental for the prediction of RUL under anticipated future usage. Failure of electronic devices is of great concern as future aircraft will see an increase of electronics to drive and control safety-critical equipment throughout the aircraft. Therefore, development of prognostics solutions for electronics is of key importance. This paper presents an accelerated aging system for gate-controlled power transistors. This system allows for the understanding of the effects of failure mechanisms, and the identification of leading indicators of failure which are essential in the development of physics-based degradation models and RUL prediction. In particular, this system isolates electrical overstress from thermal overstress. Also, this system allows for a precise control of internal temperatures, enabling the exploration of intrinsic failure mechanisms not related to the device packaging. By controlling the temperature within safe operation levels of the device, accelerated aging is induced by electrical overstress only, avoiding the generation of thermal cycles. The temperature is controlled by active thermal-electric units. Several electrical and thermal signals are measured in-situ and recorded for further analysis in the identification of leading indicators of failures. This system, therefore, provides a unique capability in the exploration of different failure mechanisms and the identification of precursors of failure that can be used to provide a health management solution for electronic devices.
Qualified Synthetic Dataset 2.0 for Semiconductor Order Lead Times
<p>This is the updated version of the prior uploaded dataset. It contains the most recent data.</p> <p>The created data is collected from an algorithm for measuring Infineon's customer Order Lead Times. Due to the frequent changes in e.g. volumes or Confirmed Delivery dates, an algorithm is necessary to calculate and thereby measure the correct Order Lead Times since the SAP data is misleading here. As the Lead Time data is confidential, Qualified Synthetic data will be created that share the same distribution and characteristics, but do not depict sensible customer information. The distribution of products and customers will be taken into account as well, but in encoded form both for security and confidentiality reasons. The final table of data will include Product Line, Business Month, Order Entry date, Requested and confirmed order lead time, customer name encoded, product name encoded, Order number and order volume. This is the updated dataset, containing additional data points.</p> <p> </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825225. <a href="https://safe-deed.eu/">https://safe-deed.eu/</a></p>
Qualified Synthetic Dataset 3.0 for Semiconductor Order Lead Times
<p>This is the updated version (3.0) of the prior uploaded dataset. It contains the most recent data.</p> <p>The created data is collected from an algorithm for measuring Infineon's customer Order Lead Times. Due to the frequent changes in e.g. volumes or Confirmed Delivery dates, an algorithm is necessary to calculate and thereby measure the correct Order Lead Times since the SAP data is misleading here. As the Lead Time data is confidential, Qualified Synthetic data will be created that share the same distribution and characteristics, but do not depict sensible customer information. The distribution of products and customers will be taken into account as well, but in encoded form both for security and confidentiality reasons. The final table of data will include Product Line, Business Month, Order Entry date, Requested and confirmed order lead time, customer name encoded, product name encoded, Order number and order volume. This is the updated dataset, containing additional data points.</p> <p> </p> <p> </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825225. <a href="https://safe-deed.eu/">https://safe-deed.eu/</a></p>
Anomaly Detection in Semiconductor Wafer Fabrication Using Stream Processing Systems: A Case Study - Dataset
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.