Do you want to publish a course? Click here

Fast and Flexible CCD Driver System Using Fast DAC and FPGA

41   0   0.0 ( 0 )
 Added by Emi Miyata
 Publication date 2000
  fields Physics
and research's language is English




Ask ChatGPT about the research

We have developed a completely new type of general-purpose CCD data acquisition system which enables one to drive any type of CCD using any type of clocking mode. A CCD driver system widely used before consisted of an analog multiplexer (MPX), a digital-to-analog converter (DAC), and an operational amplifier. A DAC is used to determine high and low voltage levels and the MPX selects each voltage level using a TTL clock. In this kind of driver board, it is difficult to reduce the noise caused by a short of high and low level in MPX and also to select many kinds of different voltage levels. Recent developments in semiconductor IC enable us to use a very fast sampling ($sim$ 10MHz) DAC with low cost. We thus develop the new driver system using a fast DAC in order to determine both the voltage level of the clock and the clocking timing. We use FPGA (Field Programmable Gate Array) to control the DAC. We have constructed the data acquisition system and found that the CCD functions well with our new system. The energy resolution of Mn K$alpha$ has a full-width at half-maximum of $simeq$ 150 eV and the readout noise of our system is $simeq$ 8 e$^-$.



rate research

Read More

Todays data analytics frameworks are compute-centric, with analytics execution almost entirely dependent on the pre-determined physical structure of the high-level computation. Relegating intermediate data to a second class entity in this manner hurts flexibility, performance, and efficiency. We present Whiz, a new analytics framework that cleanly separates computation from intermediate data. It enables runtime visibility into data via programmable monitoring, and data-driven computation (where intermediate data values drive when/what computation runs) via an event abstraction. Experiments with a Whiz prototype on a large cluster using batch, streaming, and graph analytics workloads show that its performance is 1.3-2x better than state-of-the-art.
89 - Qi Shen , Lei Zhao , Shubin Liu 2013
Up to the present, the wave union method can achieve the best timing performance in FPGA based TDC designs. However, it should be guaranteed in such a structure that the non-thermometer code to binary code (NTH2B) encoding process should be finished within just one system clock cycle. So the implementation of the NTH2B encoder is quite challenging considering the high speed requirement. Besides, the high resolution wave union TDC also demands the encoder to convert an ultra-wide input code to a binary code. We present a fast improved fat tree encoder (IFTE) to fulfill such requirements, in which bubble error suppression is also integrated. With this encoder scheme, a wave union TDC with 7.7 ps RMS and 3.8 ps effective bin size was implemented in an FPGA from Xilinx Virtex 5 family. An encoding time of 8.33 ns was achieved for a 276-bit non-thermometer code to a 9-bit binary code conversion. We conducted a series of tests on the oscillating period of the wave union launcher, as well as the overall performance of the TDC; test results indicate that the IFTE works well. In fact, in the implementation of this encoder, no manual routing or special constrains were required; therefore, this IFTE structure could also be further applied in other delay chain based FPGA TDCs.
A novel calorimeter sensor for electron, photon and hadron energy measurement based on Secondary Emission(SE) to measure ionization is described, using sheet-dynodes directly as the active detection medium; the shower particles in an SE calorimeter cause direct secondary emission from dynode arrays comprising the sampling or absorbing medium. Data is presented on prototype tests and Monte Carlo simulations. This sensor can be made radiation hard at GigaRad levels, is easily transversely segmentable at the mm scale, and in a calorimeter has energy signal rise-times and integration comparable to or better than plastic scintillation/PMT calorimeters. Applications are mainly in the energy and intensity frontiers.
The goal of this paper is to design image classification systems that, after an initial multi-task training phase, can automatically adapt to new tasks encountered at test time. We introduce a conditional neural process based approach to the multi-task classification setting for this purpose, and establish connections to the meta-learning and few-shot learning literature. The resulting approach, called CNAPs, comprises a classifier whose parameters are modulated by an adaptation network that takes the current tasks dataset as input. We demonstrate that CNAPs achieves state-of-the-art results on the challenging Meta-Dataset benchmark indicating high-quality transfer-learning. We show that the approach is robust, avoiding both over-fitting in low-shot regimes and under-fitting in high-shot regimes. Timing experiments reveal that CNAPs is computationally efficient at test-time as it does not involve gradient based adaptation. Finally, we show that trained models are immediately deployable to continual learning and active learning where they can outperform existing approaches that do not leverage transfer learning.
The functions of the Low-Level Radio Frequency (LLRF) system at European Spallation Source (ESS) are implemented on different Field-Programmable Gate Array (FPGA) boards in a Micro Telecommunications Computing Architecture (MTCA) crate. Besides the algorithm, code that provides access to the peripherals connected to the FPGA is necessary. In order to provide a common platform for the FPGA developments at ESS - the ESS FPGA Framework has been designed. The framework facilitates the integration of different algorithms on different FPGA boards. Three functions are provided by the framework: (1) Communication interfaces to peripherals, e.g. Analog-to-Digital Converters (ADCs) and on-board memory, (2) Upstream communication with the control system over Peripheral Component Interconnect Express (PCIe), and (3) Configuration of the on-board peripherals. To keep the framework easily extensible by Intellectual Property (IP) blocks and to enable seamless integration with the Xilinx design tools, the Advanced eXtensible Interface version 4 (AXI4) bus is the chosen communication interconnect. Furthermore, scripts automatize the building of the FPGA configuration, software components and the documentation. The LLRF control algorithms have been successfully integrated into the framework.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا