Filters

2018 - 201922020 - 20211

More filters

2021 - 20233
Reset filters

Settings

DDC: 600 × DDC: 620 × WGL Institute: IHP ×

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Multilevel HfO2-based RRAM devices for low-power neuromorphic networks
    (Melville, NY : AIP Publ., 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).
  • Thumbnail Image
    Item
    Modulation Linearity Characterization of Si Ring Modulators
    (Washington, DC : OSA, 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.
  • Thumbnail Image
    Item
    The thermal stability of epitaxial GeSn layers
    (Melville, NY : AIP Publ., 2018) Zaumseil, P.; Hou, Y.; Schubert, M.A.; von den Driesch, N.; Stange, D.; Rainko, D.; Virgilio, M.; Buca, D.; Capellini, G.
    We report on the direct observation of lattice relaxation and Sn segregation of GeSn/Ge/Si heterostructures under annealing. We investigated strained and partially relaxed epi-layers with Sn content in the 5 at. %-12 at. % range. In relaxed samples, we observe a further strain relaxation followed by a sudden Sn segregation, resulting in the separation of a β-Sn phase. In pseudomorphic samples, a slower segregation process progressively leads to the accumulation of Sn at the surface only. The different behaviors are explained by the role of dislocations in the Sn diffusion process. The positive impact of annealing on optical emission is also discussed.