Simulation of new p-type pixel strip detector with enhanced multiplication effect

This Master’s Thesis is dedicated to the simulation of new p-type pixel strip detector with enhanced multiplication effect. It is done for high-energy physics experiments upgrade such as Super Large Hadron Collider especially for Compact Muon Solenoid particle track silicon detectors. These detectors are used in very harsh radiation environment and should have good radiation hardness. The device engineering technology for developing more radiation hard particle detectors is used for minimizing the radiation degradation. New detector structure with enhanced multiplication effect is proposed in this work. There are studies of electric field and electric charge distribution of conventional and new p-type detector under reverse voltage bias and irradiation. Finally, the dependence of the anode current from the applied cathode reverse voltage bias under irradiation is obtained in this Thesis. For simulation Silvaco Technology Computer Aided Design software was used. Athena was used for creation of doping profiles and device structures and Atlas was used for getting electrical characteristics of the studied devices. The program codes for this software are represented in Appendixes.

Modeling Front End Electronics for a Gas Electron Multiplier Detector

The Large Hadron Collider (LHC) in The European Organization for Nuclear Research (CERN) will have a Long Shutdown sometime during 2017 or 2018. During this time there will be maintenance and a possibility to install new detectors. After the shutdown the LHC will have a higher luminosity. A promising new type of detector for this high luminosity phase is a Triple-GEM detector. During the shutdown these detectors will be installed at the Compact Muon Solenoid (CMS) experiment. The Triple-GEM detectors are now being developed at CERN and alongside also a readout ASIC chip for the detector. In this thesis a simulation model was developed for the ASICs analog front end. The model will help to carry out more extensive simulations and also simulate the whole chip before the whole design is finished. The proper functioning of the model was tested with simulations, which are also presented in the thesis.

Readout architectures for hybrid pixel detector readout chips

The original contribution of this thesis to knowledge are novel digital readout architectures for hybrid pixel readout chips. The thesis presents asynchronous bus-based architecture, a data-node based column architecture and a network-based pixel matrix architecture for data transportation. It is shown that the data-node architecture achieves readout efficiency 99% with half the output rate as a bus-based system. The network-based solution avoids “broken” columns due to some manufacturing errors, and it distributes internal data traffic more evenly across the pixel matrix than column-based architectures. An improvement of > 10% to the efficiency is achieved with uniform and non-uniform hit occupancies. Architectural design has been done using transaction level modeling (TLM) and sequential high-level design techniques for reducing the design and simulation time. It has been possible to simulate tens of column and full chip architectures using the high-level techniques. A decrease of > 10 in run-time is observed using these techniques compared to register transfer level (RTL) design technique. Reduction of 50% for lines-of-code (LoC) for the high-level models compared to the RTL description has been achieved. Two architectures are then demonstrated in two hybrid pixel readout chips. The first chip, Timepix3 has been designed for the Medipix3 collaboration. According to the measurements, it consumes < 1 W/cm^2. It also delivers up to 40 Mhits/s/cm^2 with 10-bit time-over-threshold (ToT) and 18-bit time-of-arrival (ToA) of 1.5625 ns. The chip uses a token-arbitrated, asynchronous two-phase handshake column bus for internal data transfer. It has also been successfully used in a multi-chip particle tracking telescope. The second chip, VeloPix, is a readout chip being designed for the upgrade of Vertex Locator (VELO) of the LHCb experiment at CERN. Based on the simulations, it consumes < 1.5 W/cm^2 while delivering up to 320 Mpackets/s/cm^2, each packet containing up to 8 pixels. VeloPix uses a node-based data fabric for achieving throughput of 13.3 Mpackets/s from the column to the EoC. By combining Monte Carlo physics data with high-level simulations, it has been demonstrated that the architecture meets requirements of the VELO (260 Mpackets/s/cm^2 with efficiency of 99%).

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Partikkelikokojakaumamenetelmän testaus INCA Feature

Tässä työssä testattiin partikkelikokojakaumien analysoinnissa käytettävää kuvankäsittelyohjelmaa INCA Feature. Partikkelikokojakaumat määritettiin elektronimikroskooppikuvista INCA Feature ohjelmaa käyttäen partikkeleiden projektiokuvista päällystyspigmenttinä käytettävälle talkille ja kahdelle eri karbonaattilaadulle. Lisäksi määritettiin partikkelikokojakaumat suodatuksessa ja puhdistuksessa apuaineina käytettäville piidioksidi- ja alumiinioksidihiukkasille. Kuvankäsittelyohjelmalla määritettyjä partikkelikokojakaumia verrattiin partikkelin laskeutumisnopeuteen eli sedimentaatioon perustuvalla SediGraph 5100 analysaattorilla ja laserdiffraktioon perustuvalla Coulter LS 230 menetelmällä analysoituihin partikkelikokojakaumiin. SediGraph 5100 ja kuva-analyysiohjelma antoivat talkkipartikkelien kokojakaumalle hyvin samankaltaisen keskiarvon. Sen sijaan Coulter LS 230 laitteen antama kokojakauman keskiarvo poikkesi edellisistä. Kaikki vertailussa olleet partikkelikokojakaumamenetelmät asettivat eri näytteiden partikkelit samaan kokojärjestykseen. Kuitenkaan menetelmien tuloksia ei voida numeerisesti verrata toisiinsa, sillä kaikissa käytetyissä analyysimenetelmissä partikkelikoon mittaus perustuu partikkelin eri ominaisuuteen. Työn perusteella kaikki testatut analyysimenetelmät soveltuvat paperipigmenttien partikkelikokojakaumien määrittämiseen. Tässä työssä selvitettiin myös kuva-analyysiin tarvittava partikkelien lukumäärä, jolla analyysitulos on luotettava. Työssä todettiin, että analysoitavien partikkelien lukumäärän tulee olla vähintään 300 partikkelia. Liian suuri näytemäärä lisää kokojakauman hajontaa ja pidentää analyysiin käytettyä aikaa useaan tuntiin. Näytteenkäsittely vaatii vielä lisää tutkimuksia, sillä se on tärkein ja kriittisin vaihe SEM ja kuva-analyysiohjelmalla tehtävää partikkelikokoanalyysiä. Automaattisten mikroskooppien yleistyminen helpottaa ja nopeuttaa analyysien tekoa, jolloin menetelmän suosio tulee kasvamaan myös paperipigmenttien tutkimuksessa. Laitteiden korkea hinta ja käyttäjältä vaadittava eritysosaaminen tulevat rajaamaan käytön ainakin toistaiseksi tutkimuslaitoksiin.