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144 results for “semiconductors”
Supporting Data: Complementary Organic Logic Gates on Plastic Formed by Self-Aligned Transistors with Gravure and Inkjet Printed Dielectric and Semiconductors
<p>The file contains the supporting data for the publication:</p> <p>S.G. Higgins, B.V.O. Muir, G. Dell'Erba, A. Perinot, M. Caironi, A.J. Campbell. Complementary Organic Logic Gates on Plastic Formed by Self-Aligned Transistors with Gravure and Inkjet Printed Dielectric and Semiconductors. doi: 10.1002/aelm.201500272. <em>Advanced Electronic Materials </em>(2015)</p> <p>See 'README.txt' for a description of the contents of the compressed file.</p>
Supporting Data: Indacenodithiophene-benzothiadiazole Organic Field-Effect Transistors with Gravure Printed Semiconductor and Dielectric on Plastic
<p>The file contains the supporting data for the publication:</p> <p>S.G. Higgins, B.V.O. Muir, M. Heeney, A.J. Campbell. Indacenodithiophene-benzothiadiazole Organic Field-Effect Transistors with Gravure Printed Semiconductor and Dielectric on Plastic. doi: 10.1557/mrc.2015.66. <em>MRS Communications</em> (2015)</p> <p>See 'README.txt' for a description of the contents of the compressed file.</p>
Dataset underlying the manuscript: MAViS: Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays
<p>Datasets underlying the manuscript. Information on how to run the scripts is detailed in the README file.</p>
Replication Data for Lead-Free Semiconductors, Phase-Evolution and Superior Stability of Multinary Tin Chalcohalides
<p>Tin-based semiconductors are highly desirable materials for energy applications due to their low toxicity and biocompatibility relative to analogous lead-based semiconductors. In particular, tin-based<br>chalcohalides possess optoelectronic properties that are ideal for photovoltaic and photocatalytic applications. In addition, they are believed to benefit from increased stability compared with halide perovskites.<br>However, to fully realize their potential, it is first necessary to better understand and predict the synthesis and phase evolution of these complex materials. Here, we describe a versatile solution-phase method for the<br>preparation of the multinary tin chalcohalide semiconductors Sn2SbS2I3, Sn2BiS2I3, Sn2BiSI5, and Sn2SI2. We demonstrate how certain thiocyanate precursors are selective toward the synthesis of chalcohalides, thus<br>preventing the formation of binary and other lower order impurities rather than the preferred multinary compositions. Critically, we utilized 119Sn ssNMR spectroscopy to further assess the phase purity of these materials. Further, we validate that the tin chalcohalides exhibit excellent water stability under ambient conditions, as well as remarkable resistance to heat over time compared to halide perovskites. Together, this work enables the isolation of lead-free, stable, direct band gap chalcohalide compositions that will help engineer more stable and biocompatible semiconductors and devices.</p>
Data associated to: Analytical Physical Model for Organic Metal-Electrolyte-Semiconductor Capacitors
<p>Data associated to the manuscript entitled: Analytical Physical Model for Organic Metal-Electrolyte-Semiconductor Capacitors by Larissa Huetter, Adrica Kyndiah and Gabriel Gomila</p>
Ion Implantation Sensor and Process Target Data for Predicting Ion Beam Tuning in Semiconductor Manufacturing
<h2><strong>Dataset Description:</strong></h2> <p>This dataset is designed to predict ion beam tuning setup processes in semiconductor manufacturing, in terms of tuning success or failure, and tuning duration. It is split into <strong><code>X</code></strong> and <code><strong>y</strong></code> to allow for supervised learning approaches.</p> <ul> <li><code><strong>X</strong></code> represents the current equipment condition and the process targets of the currently processed and the upcoming lot, as defined within recipes.</li> <li><code><strong>y</strong></code> represents the ion beam tuning setup report, which informs about the tuning success ratio and tuning duration. These setups are necessary, when switching between recipes to prepare the equipment for processing the next lot. <strong><code>y</code></strong> contains three labels, enabling classification of (1) tuning success or fail, and (2) prolonged tuning, as well as (3) estimation of tuning duration as a regression task.</li> </ul> <p>About <strong><code>X</code></strong>:</p> <p>Each lot is processed with a specific recipe to achieve the process target. The tuning takes place before the first wafer of the to-be-tuned recipe is processed. Each row in <strong><code>X</code></strong> includes logistical information such as the equipment used for processing and parsed recipe / process target information for the current and upcoming lot. The majority of data consists out of aggregated metrics of equipment-internally tracked sensor traces, recording physical parameters such as gas flows, temperatures, voltages and currents. When analyzed in conjunction with the processed recipe, these sensors provide insights into the current equipment condition. </p> <p>About <code><strong>y</strong></code>:</p> <p>The <code>setup_result</code> column indicates the success or failure of tuning - with <code>setup_result=0</code> indicating tuning success, while <code>setup_result=1</code> signals tuning failure. If the first tuning attempt fails, there may be follow-up attempts, but these are not included in this dataset. The <code>duration</code> column represents the tuning duration in seconds, as used for regression analysis. The <code>duration_interval</code> column is a binary label for prolonged tunings, i.e. <code>duration_interval=1</code> for instances, which take more than 6 minutes to tune.</p> <p>For reproducibility of the corresponding paper's results:</p> <ol> <li>The dataset contains the same carefully curated subset of features.</li> <li>The train_test_split() has already been performed, thus we provide <code>x_train</code> and <code>x_valid</code> separately.</li> <li>To reduce the effect of outliers in the data, the sensor data has already been scaled, as derived from <code>x_train</code>.</li> </ol> <p>In summary, these datasets (<code><strong>X</strong></code>, <code><strong>y</strong></code>) provide comprehensive information for predicting ion beam tuning in semiconductor manufacturing, making it a valuable resource for researchers and practitioners in the field.</p> <h2><strong>Python Code for Reproducibility:</strong></h2> <p>Furthermore, we share a jupyter notebook <code>ionbeamtuning.ipynb</code> with Python code to train the best performing model on the provided data, as described in the paper. To execute the code, you may need to install any missing packages specified in the <code>requirements.txt</code>, as indicated within the notebook.</p>
A set of typical relevant exposure scenarios for nanoparticles in semiconductor industry (dataset)
<p>This is an Excel database part of Deliverable 1.3 "A set of typical relevant exposure scenarios for NP’s in semiconductor industry"</p> <p>https://www.zenodo.org/record/2538388</p> <p> </p>
Photon absorption in direct bandgap semiconductor
<p>Video illustrating a photon absorption process in a direct bandgap semicondutor. The video has been created using Blender 2.81. The source file is also attached.</p>
Photon absorption in an indirect bandgap semiconductor
<p>The video illustrates the photon absorption process in an indirect bandgap semiconductor. The process is assisted by a phonon (blue ball) that provides the necessary crystal momentum. The video has been done using blender 2.81. The source file is also provided so you can edit the colors, reproduce the video from different camera angles, etc...if you wish. </p>
Software repository for data-driven reconstruction of doping profiles in semiconductors
<p>Datasets and code used described in paper: "Data-driven solutions of ill-posed inverse problems arising from doping reconstruction in semiconductors" [arXiv:2208.00742]</p>
Plasma etching for fabrication of complex nanophotonic lasers from bonded InP semiconductor layers
<p>Integrating optically active III-V materials on silicon/insulator platforms is one potential path towards improving the energy efficiency and performance of modern computing. Here we demonstrate the applicability of direct wafer bonding combined with plasma etching to the fabrication of complex nanophotonic systems out of InP layers. We explore and optimise the plasma etching of InP, validating existing processes and developing improved ones. We explore the use of microdisk lasing as a way to evaluate fabrication fidelity, and demonstrate that we can create complex lasing systems of interest to us: coupled disk cavities and random network lasers.</p> <p><strong>This repository contains data used to generate figures in <a href="https://doi.org/10.1016/j.mne.2023.100196">https://doi.org/10.1016/j.mne.2023.100196</a>.</strong></p>
Equipment Sensor Data from Semiconductor Frontend Production
<p>This data set was generated in accordance with the semiconductor industry and contains sensor recordings from high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. Typically, bunches of wafers are aggregated into so-called lots of size 25, which always pass through the same operations in the production chain.</p> <p>In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data recorded during each process step. To keep the entire production as stable as possible, the sensor data is used in order to intervene in case of deviations.</p> <p>After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given sensor data is provided. The aim is to find correlations between the wafer test data and the sensor data in order to identify the root cause.</p> <p>The given data is divided into three data sets: "equipment1.csv", "equipment2.csv" and "response.csv". "equipment1.csv" and "equipment2.csv" represent the sensor data for two process equipment. The "response.csv" data set contains the corresponding wafer test data. For the unique identification, the first two columns in each data set are the lot number and the wafer number respectively. It must be mentioned that the number of wafers contained can vary within but also between the equipment.</p> <p>The exact column structure is given as follows:</p> <ul> <li>for "equipment1.csv" and "equipment2.csv": <ul> <li>lot: the lot number</li> <li>wafer: the wafer number</li> <li>timestamp: the timestamp of the respective sensor recordings (176 timestamps per wafer - represented as approximately every second one recording for the sensors)</li> <li>sensor_1: the recordings of the first sensor</li> <li>sensor_2: the recordings of the second sensor</li> <li>...</li> <li>sensor_56: the recordings of the last sensor</li> </ul> </li> </ul> <p>"sensor_1"-"sensor_24" belongs to "equipment1" and "sensor_25"-"sensor_56" belongs to "equipment2".</p> <ul> <li>for "response.csv": <ul> <li>lot: the lot number</li> <li>wafer: the wafer number</li> <li>response: the numerical test values</li> <li>class: the "good"/"bad" classification depending on the response value (threshold: 0,75)</li> </ul> </li> </ul>
Supporting data for "Quantum simulation of a Fermi-Hubbard model using a semiconductor quantum dot array"
<p>Supporting data and analysis scripts for Fig. 3b of "Quantum simulation of a Fermi-Hubbard model using a semiconductor quantum dot array", ArXiv:1702.07511 (preprint) and 10.1038/nature23022 (publication)</p> <p>This dataset contains a readme file as well as three zipped folders that contain (1) raw data sets of all relevant measurements, as well as (2) matlab files to plot fitted data and the extracted parameters and (3) the code that uses the extracted parameters to plot the fan diagram.</p>
Tomography Data for: Three-dimensional Nanoscale Metal, Metal Oxide and Semiconductor Frameworks through DNA-programmable Assembly and Templating
<p>This is data collected at the 3-ID Hard X-ray Nanoprobe beamline. This repository supports the following research article: </p><p>Data provided is the aligned dataset and reconstruction using a FISTA algorithm. Angles Collected -90 to +45 at 1 degree steps. </p><p><strong>Three-dimensional Nanoscale Metal, Metal Oxide and Semiconductor Frameworks through DNA-programmable Assembly and Templating</strong></p><p>By Aaron Michelson. </p><p> </p>
Replication Data for Coloring Tetrahedral Semiconductors: Synthesis and Photoluminescence Enhancement of Ternary II-III2‑VI4 Colloidal Nanocrystals
<p>Ternary tetrahedral II-III2-VI4 semiconductors, where II is Zn or Cd, III In or Ga, and VI S, Se, or Te, are of interest in UV radiation detectors in medicine and space physics as well as CO2 photoreduction under visible light. We synthesize colloidal II-III2-VI4 semiconductor nanocrystals from readily available precursors and<br>ascertain their ternary nature by structural and spectroscopic methods, including 77Se solid-state NMR spectroscopy. The pyramidally shaped nanocrystals range between 2 and 12 nm and exhibit optical gaps of 2−3.9 eV. In the presence of excess anions on the particle surface, treatment with Lewis acidic, Z-type ligands results in better passivation and enhanced photoluminescence. Electronic structure calculations reveal<br>the most stable, lowest energy polymorphs and coloring patterns. This work will pave the way toward more environmentally friendly, ternary semiconductors for optoelectronics and electrocatalysis.</p>
Dataset of the publication: Interplay between optical emission and magnetism in the van der Waals magnetic semiconductor CrSBr in the two-dimensional limit
<p>Dataset of the publication: Interplay between optical emission and magnetism in the van der Waals magnetic semiconductor CrSBr in the two-dimensional limit</p> <p>DOI: 10.1021/acsnano.3c00375</p> <p>F. Marques-Moros, C. Boix-Constant, S. Mañas-Valero, J. Canet-Ferrer, E. Coronado</p> <p>ACS Nano, 17, 14, 13224-13231 (2023)</p>
Metadata of " Stability of Selected Hydrogen Bonded Semiconductors in Organic Electronic Devices"
<p>Metadata of " Stability of Selected Hydrogen Bonded Semiconductors in Organic Electronic Devices"</p>
dataset for Fig 2 in NatComm "Localised structuring of metal-semiconductor cores in silica clad fibres using laser-driven thermal gradients"
<p>Infrared transmission of silicon core fiber through which gold has been laser-thermally moved to reystallize the material</p>
Spin-mixing enhanced proximity effect in aluminum-based superconductor-semiconductor hybrids
<p>In superconducting quantum circuits, aluminum is one of the most widely used materials. It is currently also the superconductor of choice for the development of topological qubits. In this application, however, aluminum-based devices suffer from poor magnetic field compatibility. In this article, we resolve this limitation by showing that adatoms of heavy elements (e.g. platinum) increase the critical field of thin aluminum films by more than a factor of two. Using tunnel junctions, we show that the increased field resilience originates from spin-orbit scattering introduced by Pt. We exploit this property in the context of the superconducting proximity effect in semiconductor-superconductor hybrids, where we show that InSb nanowires strongly coupled to Al/Pt films can maintain superconductivity up to 7 T. The two-electron charging effect, a fundamental requirement for topo- logical quantum computation, is shown to be robust against the presence of heavy adatoms. Additionally, we use non-local spectroscopy in a three-terminal geom- etry to probe the bulk of hybrid devices, showing that it remains free of sub-gap states. Finally, we demonstrate that semiconductor states which are proximi- tized by Al/Pt films maintain their ability to Zeeman-split in an applied magnetic field. Combined with the chemical stability and well-known fabrication routes of aluminum, Al/Pt emerges as the natural successor to Al-based systems and is a compelling alternative to other superconductors, whenever high-field resilience is required. </p> <p> </p>
Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors
<p>The "Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors" can be used to test machine learning approaches on their capability of interpreting sensor patterns of commercially available MOS gas sensors, i.e., SGP40 (Sensirion AG, Stäfa, Switzerland), to predict multiple different gas concentrations and the relative humidity. Furthermore, the dataset can be used to test the transferability between sensors. <br> The dataset was recorded with the help of a custom-built gas mixing apparatus (GMA). The GMA allows applying well-known gas mixtures to multiple gas sensors. For this experiment, three SGP40 with four sub-sensors each were exposed to 900 different unique gas mixtures (UGMs) consisting of ten different gases. In detail, the dataset consists of eight volatile organic compounds (VOCs) (acetic acid, acetone, ethanol, ethyl acetate, formaldehyde, isopropanol, toluene, and xylene), two background gases (carbon monoxide and hydrogen), and the relative humidity at 20 °C. During exposure, the sensors are operated in a temperature-cycled operation. The temperature cycle consists of alternating high and low-temperature phases. The high-temperature phases are set at 400 °C and have a duration of 5 seconds, while the low-temperature steps increase in 25 °C steps from 100 °C-375 °C, where each step has a duration of 7 seconds. The only exception is sub-sensor 4, where the temperature is only alternated between 250 °C and 300 °C. The total duration of the temperature cycle is 144 seconds, and during this time, the logarithmic sensor resistance is read out at 10 Hz. Each gas mixture was recorded for ten temperature cycles to ensure that stable gas mixtures were applied to the sensor. Only stable samples 6 (not always),7,8, and 9 were used for further evaluation. The 900 UGMs can be separated into three parts, and for each part, the mixtures were generated based on Latin hypercube sampling and the ranges specified in Table 1.</p> <table> <caption>Tabel 1: Uniform distributed ranges for all gasses within the gas mixtures</caption> <tbody> <tr> <td> </td> <td>UGM 1-200</td> <td>UGM 201-500</td> <td>UGM501-900</td> </tr> <tr> <td>Carbon monoxide</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> </tr> <tr> <td>Hydrogen</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> </tr> <tr> <td>Relative humidity</td> <td>25 - 80 %</td> <td>25 - 80 %</td> <td>25 - 80 %</td> </tr> <tr> <td>Acetic acid</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Acetone</td> <td>3 - 50 ppb</td> <td>3 - 150 ppb</td> <td>3 - 500 ppb</td> </tr> <tr> <td>Ethanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Ethyl acetate</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Formaldehyde</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 300 ppb</td> </tr> <tr> <td>Isopropanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Toluene</td> <td>1 - 75 ppb</td> <td>1 - 75 ppb</td> <td>1 - 250 ppb</td> </tr> <tr> <td>Xylene</td> <td>2 - 150 ppb</td> <td>2 - 150 ppb</td> <td>2 - 500 ppb</td> </tr> </tbody> </table> <p>To be able to use this dataset for transfer learning, the dataset consists of three different SPG40; two are from the same batch (sensor A and sensor B), and sensor C is from a different batch. <br> The dataset consists of the sensors' data and a target for evaluation. The data is already split into training and Validation and is stored in cells for each sensor: <br> sensorA_train<br> sensorA_test<br> sensorB_train<br> sensorB_test<br> sensorC_train<br> sensorC_test</p> <p> Each sensor cell contains four arrays, one for each sub-sensor within one SGP40. The number of rows in the arrays represents the number of observations (693 for test and 2401 for training), and the number of columns represents the number of samples per observation (1440).<br> The targets, i.e., the concentrations of each gas, are given in the target_train and targe_test structs. Since the data were recorded simultaneously, those structs can be used as targets for all sensors. The ten different gases, relative humidity, and TVOCsens are actual targets, while the range parameter represents the specific unique gas mixture ID.</p> <p>Although this is a mat file, it can be opened as an hdf5 file.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.