Stabilizing the forming process in unipolar resistance switching using an improved compliance current limiter

The high reset current IR in unipolar resistance switching now poses major obstacles to practical applications in memory devices. In particular, the first IR-value after the forming process is so high that the capacitors sometimes do not exhibit reliable unipolar resistance switching. We found that the compliance current Icomp is a critical parameter for reducing IR-values. We therefore introduced an improved, simple, easy to use Icomp-limiter that stabilizes the forming process by drastically decreasing current overflow, in order to precisely control the Icomp- and subsequent IR-values.Comment: 15 pages, 4 figure

Optical Response of Solid CO2_2 as a Tool for the Determination of the High Pressure Phase

We report first-principles calculations of the frequency dependent linear and second-order optical properties of the two probable extended-solid phases of CO$_2$--V, i.e. $I\bar42d$ and $P2_12_12_1$. Compared to the parent $Cmca$ phase the linear optical susceptibility of both phases is much smaller. We find that $I\bar42d$ and $P2_12_12_1$ differ substantially in their linear optical response in the higher energy regime. The nonlinear optical responses of the two possible crystal structures differ by roughly a factor of five. Since the differences in the nonlinear optical spectra are pronounced in the low energy regime, i.e. below the band gap of diamond, measurements with the sample inside the diamond anvil cell are feasible. We therefore suggest optical experiments in comparison with our calculated data as a tool for the unambiguous identification of the high pressure phase of CO$_2$.Comment: 4 pages 2 fig

Sparse Optical Arbitrary Waveform Measurement by Compressive Sensing

We propose and experimentally demonstrate a compressive sensing scheme based on optical coherent receiver that recovers sparse optical arbitrary signals with an analog bandwidth up to 25GHz. The proposed scheme uses 16x lower sampling rate than the Nyquist theorem and spectral resolution of 24.4MHz

Evaluating CAVM: A New Search-Based Test Data Generation Tool for C

We present CAVM (pronounced “ka-boom”), a new search-based test data generation tool for C. CAVM is developed to augment an existing commercial tool, CodeScroll, which uses static analysis and input partitioning to generate test data. Unlike the current state-of-the-art search-based test data generation tool for C, Austin, CAVM handles dynamic data structures using purely search-based techniques. We compare CAVM against CodeScroll and Austin using 49 C functions, ranging from small anti-pattern case studies to real world open source code and commercial code. The results show that CAVM can cover branches that neither CodeScroll nor Austin can, while also exclusively achieving the highest branch coverage for 20 of the studied functions

Photonic spiking neural networks with event-driven femtojoule optoelectronic neurons based on Izhikevich-inspired model

Photonic spiking neural networks (PSNNs) potentially offer exceptionally high throughput and energy efficiency compared to their electronic neuromorphic counterparts while maintaining their benefits in terms of event-driven computing capability. While state-of-the-art PSNN designs require a continuous laser pump, this paper presents a monolithic optoelectronic PSNN hardware design consisting of an MZI mesh incoherent network and event-driven laser spiking neurons. We designed, prototyped, and experimentally demonstrated this event-driven neuron inspired by the Izhikevich model incorporating both excitatory and inhibitory optical spiking inputs and producing optical spiking outputs accordingly. The optoelectronic neurons consist of two photodetectors for excitatory and inhibitory optical spiking inputs, electrical transistors’ circuits providing spiking nonlinearity, and a laser for optical spiking outputs. Additional inclusion of capacitors and resistors complete the Izhikevich-inspired optoelectronic neurons, which receive excitatory and inhibitory optical spikes as inputs from other optoelectronic neurons. We developed a detailed optoelectronic neuron model in Verilog-A and simulated the circuit-level operation of various cases with excitatory input and inhibitory input signals. The experimental results closely resemble the simulated results and demonstrate how the excitatory inputs trigger the optical spiking outputs while the inhibitory inputs suppress the outputs. The nanoscale neuron designed in our monolithic PSNN utilizes quantum impedance conversion. It shows that estimated 21.09 fJ/spike input can trigger the output from on-chip nanolasers running at a maximum of 10 Gspike/second in the neural network. Utilizing the simulated neuron model, we conducted simulations on MNIST handwritten digits recognition using fully connected (FC) and convolutional neural networks (CNN). The simulation results show 90% accuracy on unsupervised learning and 97% accuracy on a supervised modified FC neural network. The benchmark shows our PSNN can achieve 50 TOP/J energy efficiency, which corresponds to 100 × throughputs and 1000 × energy-efficiency improvements compared to state-of-art electrical neuromorphic hardware such as Loihi and NeuroGrid

Performance studies of 3D-Hyper-FleX-LION for HPC applications

This paper studies the performance of 3D-Hyper-FleX-LION for HPC systems. The simulation results obtained for different HPC applications (i.e. Fill Boundary, Crystal Router, MiniFE, and MiniDFT) show up to 2.8× improvements in throughput per watt when compared with a Fat-Tree with no oversubcription

Ion-beam nanopatterning of silicon surfaces under codeposition of non-silicide-forming impurities

We report experiments on surface nanopatterning of Si targets which are irradiated with 2-keV Ar+ ions impinging at near-glancing incidence, under concurrent codeposition of Au impurities simultaneously extracted from a gold target by the same ion beam. Previous recent experiments by a number of groups suggest that silicide formation is a prerequisite for pattern formation in the presence of metallic impurities. In spite of the fact that Au is known not to form stable compounds with the Si atoms, ripples nonetheless emerge in our experiments with nanometric wavelengths and small amplitudes, and with an orientation that changes with distance to the Au source. We provide results of sample analysis through Auger electron and energy-dispersive x-ray spectroscopies for their space-resolved chemical composition, and through atomic force, scanning transmission electron, and high-resolution transmission microscopies for their morphological properties. We discuss these findings in the light of current continuum models for this class of systems. The composition of and the dynamics within the near-surface amorphized layer that ensues is expected to play a relevant role to account for the unexpected formation of these surface structures.This work was supported by NRF (Korea) Grants No. 2014K2A1A2048433 and No. 2013R1A2000245 and by MINECO (Spain) Grants No. FIS2012-32349, No. FIS2012-38866-C05-01, and No. FIS2015-66020-C2-1-

Multi-Cluster Reconfiguration with Traffic Prediction in Hyper-Flex-LION Architecture

We study the performance of Hyper-Flex-LION optical interconnect architecture under dynamic traffic with traffic-prediction-aided multi-cluster reconfiguration. The simulation results show a 17.2% latency improvement and 36.9% packet loss reduction as compared to a fixed topology

Gregory-Laflamme instability of a slowly rotating black string

We study the Gregory-Laflamme instability of a 5-dimensional slowly rotating black string in which the 4-dimensional section is described by the Kerr black hole. We treat the rotation in a perturbative way introducing a small parameter for the rotation. It is found that rotation makes the Gregory-Laflamme instability stronger. Both the critical wavelength at the onset of instability and the growth time-scale are found to decrease as the rotation increases.Comment: 26 pages, 1 figur