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End date: 2024 × Start date: 2020 × DDC: 530 × Publisher: Basel : MDPI × WGL Institute: IHP ×

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Now showing 1 - 3 of 3
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    Electron Population Dynamics in Optically Pumped Asymmetric Coupled Ge/SiGe Quantum Wells: Experiment and Models
    (Basel : MDPI, 2020) Ciano, Chiara; Virgilio, Michele; Bagolini, Luigi; Baldassarre, Leonetta; Rossetti, Andrea; Pashkin, Alexej; Helm, Manfred; Montanari, Michele; Persichetti, Luca; Di Gaspare, Luciana; Capellini, Giovanni; Paul, Douglas J.; Scalari, Giacomo; Faist, Jèrome; De Seta, Monica; Ortolani, Michele
    n-type doped Ge quantum wells with SiGe barriers represent a promising heterostructure system for the development of radiation emitters in the terahertz range such as electrically pumped quantum cascade lasers and optically pumped quantum fountain lasers. The nonpolar lattice of Ge and SiGe provides electron-phonon scattering rates that are one order of magnitude lower than polar GaAs. We have developed a self-consistent numerical energy-balance model based on a rate equation approach which includes inelastic and elastic inter-and intra-subband scattering events and takes into account a realistic two-dimensional electron gas distribution in all the subband states of the Ge/SiGe quantum wells by considering subband-dependent electronic temperatures and chemical potentials. This full-subband model is compared here to the standard discrete-energy-level model, in which the material parameters are limited to few input values (scattering rates and radiative cross sections). To provide an experimental case study, we have epitaxially grown samples consisting of two asymmetric coupled quantum wells forming a three-level system, which we optically pump with a free electron laser. The benchmark quantity selected for model testing purposes is the saturation intensity at the 1!3 intersubband transition. The numerical quantum model prediction is in reasonable agreement with the experiments and therefore outperforms the discrete-energy-level analytical model, of which the prediction of the saturation intensity is off by a factor 3. © 2019 by the authors.
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    Modulating the Filamentary-Based Resistive Switching Properties of HfO2 Memristive Devices by Adding Al2O3 Layers
    (Basel : MDPI, 2022) Kalishettyhalli Mahadevaiah, Mamathamba; Perez, Eduardo; Lisker, Marco; Schubert, Markus Andreas; Perez-Bosch Quesada, Emilio; Wenger, Christian; Mai, Andreas
    The resistive switching properties of HfO2 based 1T-1R memristive devices are electrically modified by adding ultra-thin layers of Al2 O3 into the memristive device. Three different types of memristive stacks are fabricated in the 130 nm CMOS technology of IHP. The switching properties of the memristive devices are discussed with respect to forming voltages, low resistance state and high resistance state characteristics and their variabilities. The experimental I–V characteristics of set and reset operations are evaluated by using the quantum point contact model. The properties of the conduction filament in the on and off states of the memristive devices are discussed with respect to the model parameters obtained from the QPC fit.
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    Optimization of Multi-Level Operation in RRAM Arrays for In-Memory Computing
    (Basel : MDPI, 2021) Pérez, Eduardo; Pérez-Ávila, Antonio Javier; Romero-Zaliz, Rocío; Mahadevaiah, Mamathamba Kalishettyhalli; Pérez-Bosch Quesada, Emilio; Roldán, Juan Bautista; Jiménez-Molinos, Francisco; Wenger, Christian
    Accomplishing multi-level programming in resistive random access memory (RRAM) arrays with truly discrete and linearly spaced conductive levels is crucial in order to implement synaptic weights in hardware-based neuromorphic systems. In this paper, we implemented this feature on 4-kbit 1T1R RRAM arrays by tuning the programming parameters of the multi-level incremental step pulse with verify algorithm (M-ISPVA). The optimized set of parameters was assessed by comparing its results with a non-optimized one. The optimized set of parameters proved to be an effective way to define non-overlapped conductive levels due to the strong reduction of the device-to-device variability as well as of the cycle-to-cycle variability, assessed by inter-levels switching tests and during 1 k reset-set cycles. In order to evaluate this improvement in real scenarios, the experimental characteristics of the RRAM devices were captured by means of a behavioral model, which was used to simulate two different neuromorphic systems: an 8 × 8 vector-matrix-multiplication (VMM) accelerator and a 4-layer feedforward neural network for MNIST database recognition. The results clearly showed that the optimization of the programming parameters improved both the precision of VMM results as well as the recognition accuracy of the neural network in about 6% compared with the use of non-optimized parameters.