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Projekt ultraszybkich układów LVDS w technologii nanometrycznej dla potrzeb obrazowania w medycynie

Kmon P., Satława T.

Przegląd Elektrotechniczny

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2014

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R. 90, nr 5

106--109

PL

W pracy przedstawiono projekt oraz wyniki symulacji ultraszybkich układów LVDS w technologii nanometrycznej dla potrzeb obrazowania w medycynie. Projekt dotyczy układów nadajnika oraz odbiornika i został wykonany w nowoczesnej technologii CMOS 40nm. Głównymi wymaganiami prezentowanej pracy był mały pobór mocy zarówno statycznej jak i dynamicznej oraz mała zajętość powierzchni układów. Blok nadajnika oparty jest o architekturę przełączanego mostka prądowego zaś głównym elementem odbiornika jest konwerter poziomów logicznych i komparator z histerezą. Układ odbiorczy pobiera 7.08 mW mocy statycznej i 12.09 mW mocy dynamicznej zaś układ nadawczy 17.93 mW mocy statycznej i 26.38 mW mocy dynamicznej. Wyniki symulacji pokazują poprawną pracę układów przy prędkości transmisji 1 Gb/s i obciążeniu nadajnika pojemnością 5 pF. Układ odbiorczy zajmuje powierzchnię 0.009 mm2 zaś nadawczy 0.1 mm2.

EN

The paper presents the design and simulation results of ultrafast I/O interface in nanometer process. Both, the receiver and the transmitter are designed in CMOS 40nm process and are dedicated to work with a multichannel pixel integrated circuit that is destined for medical imaging systems. The main requirements of a project are a low power consumption and small area occupation of the LVDS circuits. The transmitter is based on the current switching bridge while the receiver is built of the logic converter and inverting comparator with hysteresis. The receiver block dissipates 7.08 mW of static power and 12.09 mW of dynamic power while the transmitter dissipates 17.93 mW of static power and 26.38 mW of dynamic power at 1 GHz signal and 5pF load. The receiver and transmitter occupy respectively 0.009 mm2 and 0.1 mm2 of chip area.

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Trends in hybrid pixel detectors for X-ray imaging using deep submicron VLSI technology

Drozd A., Satława T.

Challenges of Modern Technology

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2014

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Vol. 5, no. 4

3--7

EN

The article covers the latest developments in pixel detectors used for X-ray imaging as well as the description of the practical solution – a multichannel integrated circuit dedicated to X-ray imaging. In general introduction a wide range of pixel detector applications is presented. The main part focuses on the challenges and new solutions for the field of X-ray imaging, including 3D integration, silicon-on-insulator and submicron technologies. Since minimization of a pixel size together with implementing more functionality are important issues in the detectors’ and integrated circuits’ design, the aspects of channel-to-channel uniformity and additional effects like charge sharing between pixels are taken into consideration. In the last section, the Authors present the application specific integrated circuit designed in 40 nm technology dedicated to X-ray detection and future prospects are discussed.

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Application of multidimensional data analysis to chromatography

Satława T., Grabska-Chrząstowska J., Korohoda P.

Image Processing & Communications

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2013

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Vol. 18, no. 2-3

101--108

EN

This work presents analysis of chromatographic signal used to identify substances in samples. First part consists of chromatography overview and description of three classification methods (neural network with backpropagation, probabilistic neural network with Parzen window and support vector machines). Designed algorithm consists of several stages: signal filtering, peak detection and its approximation with sum of two Gaussian functions. The parameters of that two curves are the features vectors describing the peak of the substance. The last step is classification, for which two types of supervised machine learning were compared, based on the whole signal and on features vectors. Both types were tested for different classificators and their parameters. Verification was based on 55 chromatography signals. The best results for both methods of learning were achieved for probabilistic neural networks. The correct classification rate was 82% for the whole signal and 93% for feature vectors.