2022, Marzban, Bahareh, Seidel, Lukas, Liu, Teren, Wu, Kui, Kiyek, Vivien, Zoellner, Marvin Hartwig, Ikonic, Zoran, Schulze, Joerg, Grützmacher, Detlev, Capellini, Giovanni, Oehme, Michael, Witzens, Jeremy, Buca, Dan

SiGeSn holds great promise for enabling fully group-IV integrated photonics operating at wavelengths extending in the mid-infrared range. Here, we demonstrate an electrically pumped GeSn microring laser based on SiGeSn/GeSn heterostructures. The ring shape allows for enhanced strain relaxation, leading to enhanced optical properties, and better guiding of the carriers into the optically active region. We have engineered a partial undercut of the ring to further promote strain relaxation while maintaining adequate heat sinking. Lasing is measured up to 90 K, with a 75 K T0. Scaling of the threshold current density as the inverse of the outer circumference is linked to optical losses at the etched surface, limiting device performance. Modeling is consistent with experiments across the range of explored inner and outer radii. These results will guide additional device optimization, aiming at improving electrical injection and using stressors to increase the bandgap directness of the active material.

2017, Bramburger, Stefan, Zinke, Benny, Killat, Dirk

This paper presents a modified interpolation algorithm for signals with variable data rate from asynchronous ADCs. The Adaptive weights Conjugate gradient Toeplitz matrix (ACT) algorithm is extended to operate with a continuous data stream. An additional preprocessing of data with constant and linear sections and a weighted overlap of step-by-step into spectral domain transformed signals improve the reconstruction of the asycnhronous ADC signal. The interpolation method can be used if asynchronous ADC data is fed into synchronous digital signal processing.

2019, Wang, Qiang, Niu, Gang, Roy, Sourav, Wang, Yankun, Zhang, Yijun, Wu, Heping, Zhai, Shijie, Bai, Wei, Shi, Peng, Song, Sannian, Song, Zhitang, Xie, Ya-Hong, Ye, Zuo-Guang, Wenger, Christian, Meng, Xiangjian, Ren, Wei

There was an error in the author list of this published article. The corresponding authors for this paper are Gang Niu (gangniu@xjtu.edu.cn) and Wei Ren (wren@mail.xjtu.edu.cn). The footnote indicating that Qiang Wang and Gang Niu contributed equally to the work was not intended. The corrected author list and notations are shown here. The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers. © The Royal Society of Chemistry 2019.

2022, Powroźnik, Piotr, Szcześniak, Paweł, Sobolewski, Łukasz, Piotrowski, Krzysztof

Energy management in power systems is influenced by such factors as economic and ecological aspects. Increasing the use of electricity produced at a given time from renewable energy sources (RES) by employing the elastic energy management algorithm will allow for an increase in “green energy“ in the energy sector. At the same time, it can reduce the production of electricity from fossil fuels, which is a positive economic aspect. In addition, it will reduce the volume of energy from RES that have to be stored using expensive energy storage or sent to other parts of the grid. The model parameters proposed in the elastic energy management algorithm are discussed. In particular, attention is paid to the time shift, which allows for the acceleration or the delay in the start-up of smart appliances. The actions taken by the algorithm are aimed at maintaining a compromise between the user’s comfort and the requirements of distribution network operators. Establishing the value of the time shift parameter is based on GMDH neural networks and the regression method. In the simulation studies, the extension of selected activities related to the tasks performed in households and its impact on the user’s comfort as well as the response to the increased generation of energy from renewable energy sources have been verified by the simulation research presented in this article. The widespread use of the new functionalities of smart appliance devices together with the elastic energy management algorithm is planned for the future. Such a combination of hardware and software will enable more effective energy management in smart grids, which will be part of national power systems.

2020, Vitale, Francesco, De Matteis, Fabio, Casalboni, Mauro, Prosposito, Paolo, Steglich, Patrick, Ksianzou, Viachaslau, Breiler, Christian, Schrader, Sigurd, Paci, Barbara, Generosi, Amanda, Prosposito, Paolo

The present work is concerned with the investigation of the nonlinear optical response of green emissive CsPbBr3 nanocrystals, in the form of colloidal dispersions in toluene, synthesized via a room-temperature ligand-assisted supersaturation recrystallization (LASR) method. After carrying out a preliminary characterization via X-Ray Diffraction (XRD) and Absorption and Photoluminescence (PL) Spectroscopies, the optical nonlinearity of the as-obtained colloids is probed by means of a single-beam Z-scan setup. Results show that the material in question, within the sensitivity of the experimental apparatus, exhibits a nonlinear refractive index n2 that is the order of 10-15 cm2/W. Moreover, a three-photon absorption mechanism (3PA) is postulated, according to the fitting of the recorded Z-scan traces and the fundamental absorption threshold, which turns out to be off resonance with twice the energy of the laser radiation. A figure of merit is, then, calculated as an indicator of the quality of the CsPbBr3 nanocrystals as a candidate material for photonic devices, for instance, Kerr-like electro-optic modulators (EOMs).

2020, Ettehad, Honeyeh Matbaechi, Soltani Zarrin, Pouya, Hölzel, Ralph, Wenger, Christian

This paper presents a dielectrophoretic system for the immobilization and separation of live and dead cells. Dielectrophoresis (DEP) is a promising and efficient investigation technique for the development of novel lab-on-a-chip devices, which characterizes cells or particles based on their intrinsic and physical properties. Using this method, specific cells can be isolated from their medium carrier or the mixture of cell suspensions (e.g., separation of viable cells from non-viable cells). Main advantages of this method, which makes it favorable for disease (blood) analysis and diagnostic applications are, the preservation of the cell properties during measurements, label-free cell identification, and low set up cost. In this study, we validated the capability of complementary metal-oxide-semiconductor (CMOS) integrated microfluidic devices for the manipulation and characterization of live and dead yeast cells using dielectrophoretic forces. This approach successfully trapped live yeast cells and purified them from dead cells. Numerical simulations based on a two-layer model for yeast cells flowing in the channel were used to predict the trajectories of the cells with respect to their dielectric properties, varying excitation voltage, and frequency.

2021, Jo, Youngkwan, Mai, Christian, Lischke, Stefan, Zimmermann, Lars, Choi, Woo-Young

Modulation linearity of Si ring modulators (RMs) is investigated through the numerical simulation based on the coupled-mode theory and experimental verification. Numerical values of the key parameters needed for the simulation are experimentally extracted. Simulation and measurement results agree well. With these, the influence of input optical wavelength and power on the Si RM linearity are characterized.

2019, Milo, V., Zambelli, C., Olivo, P.

Training and recognition with neural networks generally require high throughput, high energy efficiency, and scalable circuits to enable artificial intelligence tasks to be operated at the edge, i.e., in battery-powered portable devices and other limited-energy environments. In this scenario, scalable resistive memories have been proposed as artificial synapses thanks to their scalability, reconfigurability, and high-energy efficiency, and thanks to the ability to perform analog computation by physical laws in hardware. In this work, we study the material, device, and architecture aspects of resistive switching memory (RRAM) devices for implementing a 2-layer neural network for pattern recognition. First, various RRAM processes are screened in view of the device window, analog storage, and reliability. Then, synaptic weights are stored with 5-level precision in a 4 kbit array of RRAM devices to classify the Modified National Institute of Standards and Technology (MNIST) dataset. Finally, classification performance of a 2-layer neural network is tested before and after an annealing experiment by using experimental values of conductance stored into the array, and a simulation-based analysis of inference accuracy for arrays of increasing size is presented. Our work supports material-based development of RRAM synapses for novel neural networks with high accuracy and low-power consumption. © 2019 Author(s).

2022, Andjelkovic, Marko, Marjanovic, Milos, Chen, Junchao, Ilic, Stefan, Ristic, Goran, Krstic, Milos

The bulk built-in current sensor (BBICS) is a cost-effective solution for detection of energetic particle strikes in integrated circuits. With an appropriate number of BBICSs distributed across the chip, the soft error locations can be identified, and the dynamic fault-tolerant mechanisms can be activated locally to correct the soft errors in the affected logic. In this work, we introduce a pulse stretching BBICS (PS-BBICS) constructed by connecting a standard BBICS and a custom-designed pulse stretching cell. The aim of PS-BBICS is to enable the on-chip measurement of the single event transient (SET) pulse width, allowing to detect the linear energy transfer (LET) of incident particles, and thus assess more accurately the radiation conditions. Based on Spectre simulations, we have shown that for the LET from 1 to 100 MeV cm2 mg−1, the SET pulse width detected by PS-BBICS varies by 620–800 ps. The threshold LET of PS-BBICS increases linearly with the number of monitored inverters, and it is around 1.7 MeV cm2 mg−1 for ten monitored inverters. On the other hand, the SET pulse width is independent of the number of monitored inverters for LET > 4 MeV cm2 mg−1. It was shown that supply voltage, temperature and process variations have strong impact on the response of PS-BBICS.

2019, Kissinger, G., Kot, D., Costina, I., Lisker, M.

PECVD nitride layers with different layer stress ranging from about 315 MPa to −1735 MPa were deposited on silicon wafers with similar concentration of interstitial oxygen. After a thermal treatment consisting of nucleation at 650°C for 4 h or 8 h followed annealing 780°C 3 h + 1000°C 16 h in nitrogen, the profiles of the oxide precipitate density were investigated. The binding states of hydrogen in the layers was investigated by FTIR. There is a clear effect of the layer stress on oxide precipitate nucleation. The higher the compressive layer stress is the higher is a BMD peak below the front surface. If the nitride layer is removed after the nucleation anneal the BMD peak below the front surface becomes lower. It is possible to model the BMD peak below the surface by vacancy in-diffusion from the silicon/nitride interface. With increasing duration of the nucleation anneal the vacancy injection from the silicon/nitride interface decreases and with increasing compressive layer stress it increases. © The Author(s) 2019.