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Blaj, G.

Publications and source records attributed to Blaj, G..

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Hammerhead, an ultrahigh resolution ePix camera for wavelength-dispersive spectrometers

Wavelength-dispersive spectrometers (WDS) are often used in synchrotron and FEL applications where high energy resolution (in the order of eV) is important. Increasing WDS energy resolution requires increasing spatial resolution of the detectors in the dispersion direction. The common approaches are not ideal: using strip detectors loses the 2D position sensitivity (important for, e.g., background estimation) and the high counting rate; using pixel detectors with a small pitch results in complex charge sharing behaviour and typically have a small size. Developing pixel detectors with high aspect ratio and a small pitch in the wavelength dispersive direction would be ideal, however, it would require a substantial ASIC development. We present a new approach, with a novel sensor design using rectangular pixels with a high aspect ratio (between strips and pixels, further called “strixels”), and strixel redistribution to match the square pixel arrays of typical ASICs. This results in a sensor area of 17.4 mm × 77 mm, with a fine pitch of 25 µm in the horizontal direction resulting in 3072 columns and 176 rows. The sensors use ePix100 readout ASICs, leveraging their low noise (43 e - , or 180 eV rms). We present results obtained with a Hammerhead ePix100 camera, showing that the small pitch (25 µm) in the dispersion direction maximizes performance for both high and low photon occupancies, resulting in optimal WDS energy resolution. The low noise level at high photon occupancy allows precise photon counting, while at low occupancy, both the energy and the subpixel position can be reconstructed for every photon, allowing an ultrahigh resolution (in the order of 1 µm) in the dispersion direction and rejection of scattered beam and harmonics. Using strixel sensors with redistribution and flip-chip bonding to standard ePix readout ASICs results in ultrahigh position resolution (~1 µm) and low noise in WDS applications, leveraging the advantages of hybrid pixel detectors (high production yield, good availability, relatively inexpensive) while minimizing development complexity through sharing the ASIC, hardware, software and DAQ development with other versions of ePix cameras.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Performance of ePix10K, a high dynamic range, gain auto-ranging pixel detector for FELs

ePix10K is a hybrid pixel detector developed at SLAC for demanding free-electron laser (FEL) applications, providing an ultrahigh dynamic range (245 eV to 88 MeV) through gain auto-ranging. It has three gain modes (high, medium and low) and two auto-ranging modes (high-to-low and medium-to-low). The first ePix10K cameras are built around modules consisting of a sensor flip-chip bonded to 4 ASICs, resulting in 352 × 384 pixels of 100 µm x 100 µm each. We present results from extensive testing of three ePix10K cameras with FEL beams at LCLS, resulting in a measured noise floor of 245 eV rms, or 67 e - equivalent noise charge (ENC), and a range of 11 000 photons at 8 keV. We demonstrate the linearity of the response in various gain combinations: fixed high, fixed medium, fixed low, auto-ranging high to low, and auto-ranging medium-to-low, while maintaining a low noise (well within the counting statistics), a very low cross-talk, perfect saturation response at fluxes up to 900 times the maximum range, and acquisition rates of up to 480 Hz. Finally, we present examples of high dynamic range x-ray imaging spanning more than 4 orders of magnitude dynamic range (from a single photon to 11 000 photons/pixel/pulse at 8 keV). Achieving this high performance with only one auto-ranging switch leads to relatively simple calibration and reconstruction procedures. The low noise levels allow usage with long integration times at non-FEL sources. ePix10K cameras leverage the advantages of hybrid pixel detectors with high production yield and good availability, minimize development complexity through sharing the hardware, software and DAQ development with all other versions of ePix cameras, while providing an upgrade path to 5 kHz, 25 kHz and 100 kHz in three steps over the next few years, matching the LCLS-II requirements.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Ultrafast processing of pixel detector data with machine learning frameworks

Modern photon science performed at high repetition rate free-electron laser (FEL) facilities and beyond relies on 2D pixel detectors operating at increasing frequencies (towards 100 kHz at LCLS-II) and producing rapidly increasing amounts of data (towards TB/s). This data must be rapidly stored for offline analysis and summarized in real time for online feedback to the scientists. While at LCLS all raw data has been stored, at LCLS-II this would lead to a prohibitive cost; instead, enabling real time processing of pixel detector data (dark, gain, common mode, background, charge summing, subpixel position, photon counting, data summarization) allows reducing the size and cost of online processing, offline processing and storage by orders of magnitude while preserving full photon information. This could be achieved by taking advantage of the compressibility of sparse data typical for LCLS-II applications. Faced with a similar big data challenge a decade ago, computer vision stimulated revolutionary advances in machine learning hardware and software. We investigated if these developments are useful in the field of data processing for high speed pixel detectors and found that typical deep learning models and autoencoder architectures failed to yield useful noise reduction while preserving full photon information, presumably because of the very different statistics and feature sets in computer vision and radiation imaging. However, the raw performance of modern frameworks like Tensorflow inspired us to redesign in Tensorflow mathematically equivalent versions of the state-of-the-art, “classical” algorithms used at LCLS. The novel Tensorflow models resulted in elegant, compact and hardware agnostic code, gaining 1 to 2 orders of magnitude faster processing on an inexpensive consumer GPU, reducing by 3 orders of magnitude the projected cost of online analysis and compression without photon loss at LCLS-II. The novel Tensorflow models also enabled ongoing development of a pipelined hardware system expected to yield an additional 3 to 4 orders of magnitude speedup, necessary for meeting the data acquisition and storage requirements at LCLS-II, potentially enabling acquiring every single FEL pulse at full speed. Computer vision a decade ago was dominated by hand-crafted filters; their structure inspired the deep learning revolution resulting in modern deep convolutional networks; similarly, our novel Tensorflow filters provide inspiration for designing future deep learning models for ultrafast and efficient processing and classification of pixel detector images at FEL facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Optimal Pulse Processing, Pile-Up Decomposition, and Applications of Silicon Drift Detectors at LCLS

Silicon drift detectors (SDDs) revolutionized spectroscopy in fields as diverse as geology and dentistry. For a subset of experiments at ultrafast, X-ray free-electron lasers (FELs), SDDs can make substantial contributions. Often the unknown spectrum is interesting, carrying science data, or the background measurement is useful to identify unexpected signals. Many measurements involve only several discrete photon energies known a priori, allowing single-event decomposition of pile-up and spectroscopic photon counting. We designed a pulse function and demonstrated that the signal amplitude (i.e., proportional to the detected energy and obtained from fitting with the pulse function), rise time, and pulse height are interrelated, and at short peaking times, the pulse height and pulse area are not optimal estimators for detected energy; instead, the signal amplitude and rise time are obtained for each pulse by fitting, thus removing the need for pulse shaping. By avoiding pulse shaping, rise times of tens of nanoseconds resulted in reduced pulse pile-up and allowed decomposition of remaining pulse pile-up at photon separation times down to hundreds of nanoseconds while yielding time-of-arrival information with the precision of 10 ns. Waveform fitting yields simultaneously high energy resolution and high counting rates (two orders of magnitude higher than current digital pulse processors). At pulsed sources or high photon rates, photon pile-up still occurs. We showed that pile-up spectrum fitting is relatively simple and preferable to pile-up spectrum deconvolution. We then developed a photon pile-up statistical model for constant intensity sources, extended it to variable intensity sources (typical for FELs), and used it to fit a complex pileup spectrum. We subsequently developed a Bayesian pile-up decomposition method that allows decomposing pile-up of single events with up to six photons from six monochromatic lines with 99% accuracy. The usefulness of SDDs will continue into the X-ray FEL era of science. Their successors, the ePixS hybrid pixel detectors, already offer hundreds of pixels, each with a similar performance to an SDD, in a compact, robust and affordable package.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Analytical Solutions of Transient Drift-Diffusion in P-N Junction Pixel Sensors

Radiation detection in applications ranging from high energy physics to medical imaging rely on solid state detectors, often hybrid pixel detectors with (1) reverse biased p-n junction pixel sensors and (2) readout ASICs, attached by flip chip-bonding. Transient signals characteristics are important in, e.g., matching ASIC and sensor design, modeling and optimizing detector parameters and describing timing and charge sharing properties. Currently analytical forms of transient signals are available for only a few limited cases (e.g., drift or diffusion) or for the steady state (which is not relevant for high energy radiation detection). Tools are available for (relatively slow) numerical evaluation of the transient charge transport. We present here the first analytical solutions of partial differential equations de scribing drift-diffusion-recombination charge transport in planar p-n junction sensors in a variety of conditions: (1) undepleted, (2) fully depleted, (3) taking into account the gradual velocity saturation, and (4) overdepleted. We deduce the Green’s functions which can be applied to any detection problem through simple convolution with the initial conditions. We compare the analytical solutions with Monte Carlo simulations and industry standard simulations (Synopsys Sentaurus), demonstrating good agreement. Using the analytical equations enables fast modeling of the influence of various detector parameters on tracking, imaging and timing performance, describing performance and enabling optimizations for different applications. Finally, we illustrate this model with applications in 3D+T (x,y,z,time) photon tracking and 4D+T (x,y,θ,φ,time) relativistic charged particle tracking.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗