Optical pump–terahertz probe study of HR GaAs:Cr and SI GaAs:El2 structures with long charge carrier lifetimes

The time dynamics of nonequilibrium charge carrier relaxation processes in SI GaAs:EL2 (semi-insulating gallium arsenide compensated with EL2 centers) and HR GaAs:Cr (high-resistive gallium arsenide compensated with chromium) were studied by the optical pump–terahertz probe technique. Charge carrier lifetimes and contributions from various recombination mechanisms were determined at different injection levels using the model, which takes into account the influence of surface and volume Shockley–Read–Hall (SRH) recombination, interband radiative transitions and interband and trap-assisted Auger recombination. It was found that, in most cases for HR GaAs:Cr and SI GaAs:EL2, Auger recombination mechanisms make the largest contribution to the recombination rate of nonequilibrium charge carriers at injection levels above ~(0.5–3)·1018 cm−3, typical of pump–probe experiments. At a lower photogenerated charge carrier concentration, the SRH recombination prevails. The derived charge carrier lifetimes, due to the SRH recombination, are approximately 1.5 and 25 ns in HR GaAs:Cr and SI GaAs:EL2, respectively. These values are closer to but still lower than the values determined by photoluminescence decay or charge collection efficiency measurements at low injection levels. The obtained results indicate the importance of a proper experimental data analysis when applying terahertz time-resolved spectroscopy to the determination of charge carrier lifetimes in semiconductor crystals intended for the fabrication of devices working at lower injection levels than those at measurements by the optical pump–terahertz probe technique. It was found that the charge carrier lifetime in HR GaAs:Cr is lower than that in SI GaAs:EL2 at injection levels > 1016 cm−3.В ст. ошибочно: Irina A. Kolesnikov

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Algo500 – a New Approach to the Joint Analysis of Algorithms and Computers

The described project is aimed at a complete solution to the problem of jointanalysis of the properties of algorithms and features of the architecture of computing systems. This problem arose in the mid-70s of the last century, and over time, its importance in the practice of using computer systems is constantly growing. The main reason is a significant complication of the architecture of computers, which determines a strong dependence of the efficiency of their work on the properties of algorithms and programs. Exactly this dependence leads in practice to a huge gap between real and peak performance indicators, which is typical for all classes of computers from mobile devices to supercomputers of the highest performance range. It is this dependence that leads to a decrease in the quality of work of supercomputer centers and a drop in the efficiency of computer systems below a fraction of a percent. And at the same time, the fundamental nature of the problem itself determines two important facts. First, it is characteristic of all computer systems and centers of the world without exception. Second, practically all scientific groups of the world in all science areas, conducting research using high-performance computing systems, face this problem