Publikationen 2022

Script list Publications

(1) Implementation of Device-to-Device and Cycle-to-Cycle Variability of Memristive Devices in Circuit Simulations
C. Bischoff, J. Leise, E. Perez-Bosch Quesada, E. Perez, Ch. Wenger, A. Kloes
Solid State Electronics 194, 108321 (2022)
Proc. Joint International EUROSOI Workshop and International Conference on Ultimate Integration on Silicon (EUROSOI-ULIS 2022)
(KI-IoT)
We present a statistical procedure for the extraction of parameters of a compact model for memristive devices. Thereby, in a circuit simulation the typical fluctuations of the current-voltage (I-V) characteristics from device-to-device (D2D) and from cycle-to-cycle (C2C) can be emulated. The approach is based on the Stanford model whose parameters play a key role to integrating D2D and C2C dispersion. The influence of such variabilities over the model’s parameters is investigated by using a fitting algorithm fed with experimental data. After this, the statistical distributions of the parameters are used in a Monte Carlo simulation to reproduce the I-V D2D and C2C dispersions which show a good agreement to the measured curves. The results allow the simulation of the on/off current variation for the design of RRAM cells or memristor-based artificial neural networks.

(2) Vibrational Properties in Highly Strained Hexagonal Boron Nitride Bubbles
E. Blundo, A. Surrente, D. Spirito, G. Pettinari, T. Yildirim, L. Baldassarre, C.A. Chavarin, M. Felici, A. Polimeni
Nano Letters 22, 1525 (2022)

(3) Towards the Growth of hBN on Ge/Si Substrates by CVD
M. Franck, J. Dabrowski, M.A. Schubert, Ch. Wenger, M. Lukosius
Proc. Graphene 2022, 164 (2022)
(2DHetero)

(4) Mitigating the Effects of RRAM Process Variation on the Accuracy of Artifical Neural Networks
M. Fritscher, J. Knödtel, M. Mallah, S. Pechmann, E. Perez-Bosch Quesada, T. Rizzi, Ch. Wenger, M. Reichenbach
Proc. 21st International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS 2021), LNCS 13227, 401 (2022)
(KI-PRO)
In this work, three different RRAM compact models implemented in Verilog-A are analyzed and evaluated in order to reproduce the multilevel approach based on the switching capability of experimental devices. These models are integrated in 1T-1R cells to control their analog behavior by means of the compliance current imposed by the NMOS select transistor. Four different resistance levels are simulated and assessed with experimental verification to account for their multilevel capability. Further, an Artificial Neural Network study is carried out to evaluate in a real scenario the viability of the multilevel approach under study.

(5) Mitigating the Effects of RRAM Process Variation on the Accuracy of Artifical Neural Networks
M. Fritscher, J. Knödtel, M. Mallah, S. Pechmann, E. Perez-Bosch Quesada, T. Rizzi, Ch. Wenger, M. Reichenbach
Proc. 21st International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS 2021), LNCS 13227, 401 (2022)
(Total Resilience)
In this work, three different RRAM compact models implemented in Verilog-A are analyzed and evaluated in order to reproduce the multilevel approach based on the switching capability of experimental devices. These models are integrated in 1T-1R cells to control their analog behavior by means of the compliance current imposed by the NMOS select transistor. Four different resistance levels are simulated and assessed with experimental verification to account for their multilevel capability. Further, an Artificial Neural Network study is carried out to evaluate in a real scenario the viability of the multilevel approach under study.

(6) Mitigating the Effects of RRAM Process Variation on the Accuracy of Artifical Neural Networks
M. Fritscher, J. Knödtel, M. Mallah, S. Pechmann, E. Perez-Bosch Quesada, T. Rizzi, Ch. Wenger, M. Reichenbach
Proc. 21st International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS 2021), LNCS 13227, 401 (2022)
(NeuroMem)
In this work, three different RRAM compact models implemented in Verilog-A are analyzed and evaluated in order to reproduce the multilevel approach based on the switching capability of experimental devices. These models are integrated in 1T-1R cells to control their analog behavior by means of the compliance current imposed by the NMOS select transistor. Four different resistance levels are simulated and assessed with experimental verification to account for their multilevel capability. Further, an Artificial Neural Network study is carried out to evaluate in a real scenario the viability of the multilevel approach under study.

(7) Statistical Model of Program/Verify Algorithms in Resistive Switching Memories for In-Memory Neural Network Accelerators
A. Glukhov, V. Milo, A. Baroni, N. Lepri, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
Proc. International Reliability Physics Symposium (IRPS 2022), 3C.3-1 (2022)
(NeuroMem)

(8) Statistical Model of Program/Verify Algorithms in Resistive Switching Memories for In-Memory Neural Network Accelerators
A. Glukhov, V. Milo, A. Baroni, N. Lepri, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
Proc. International Reliability Physics Symposium (IRPS 2022), 3C.3-1 (2022)
(KI-PRO)

(9) Statistical Model of Program/Verify Algorithms in Resistive Switching Memories for In-Memory Neural Network Accelerators
A. Glukhov, V. Milo, A. Baroni, N. Lepri, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
Proc. International Reliability Physics Symposium (IRPS 2022), 3C.3-1 (2022)
(Total Resilience)

(10) Room Temperature Donor Incorporation for Quantum Devices: Arsine on Germanium
E.V. S. Hofmann, T.J.Z. Stock, O. Warschkow, R. Conybeare, N.J. Curson, S.R. Schofield
Materials Science (cond-mat.mtrl-sci)

(11) A Proof of Concept of the Bulk Photovoltaic Effect in Non-Uniformly Strained Silicon
C.L. Manganelli, S. Kayser, M. Virgilio
Journal of Applied Physics 131, 125706 (2022)
We numerically investigate non-uniformly strained Si-based systems to demonstrate that, when a well focused laser beam locally excites the sample, the lattice distortion, impacting the band edge profile, causes a spatially dependent photovoltaic effect. It follows that, scanning the sample surface with the pump spot, a photovoltage signal can be acquired and used to quantitatively map the non-uniform strain field. To provide numerical evidence in this direction, we combine mechanical simulations with deformation potential theory to estimate the band-edge energy landscape of a Si lattice strained by an array of SiN stripes fabricated on the top surface. These data are then used to simulate the voltage signal obtained scanning the sample surface with a normal incident pump beam. Our analysis suggests that strain deformation as small as 0.1% can trigger at room temperature robust photovoltaic signals. These results allow to envision the development of a fast, cost effective and non-destructive set-up which leverage on the bulk photovoltaic effect to image the lattice deformation in semiconductor crystals.

(12) In-Memory Principal Component Analysis by Crosspoint Array of Rresistive Switching Memory
P. Mannocci, A. Baroni, E. Melacarne, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
IEEE Nanotechnology Magazine 4 (2022)
DOI: 10.1109/TED.2021.3089995, (Total Resilience)
In-memory computing (IMC) is one of the most promising candidates for data-intensive computing accelerators of machine learning (ML). A key ML algorithm for dimensionality reduction and classification is the principal component analysis (PCA), which heavily relies on matrix-vector multiplications (MVM) for which classic von Neumann architectures are not optimized. Here, we provide the experimental demonstration of a new IMC-based PCA algorithm based on power iteration and deflation executed in a 4-kbit array of resistive switching randomaccess memory (RRAM). The classification accuracy of Wisconsin Breast Cancer dataset reaches 95.43%, close to floating-point implementation. Our simulations indicate a 250 improvement in energy efficiency compared to commercial graphic processing units (GPUs), thus supporting IMC for energy-efficient ML in modern data-intensive computing.

(13) In-Memory Principal Component Analysis by Crosspoint Array of Rresistive Switching Memory
P. Mannocci, A. Baroni, E. Melacarne, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
IEEE Nanotechnology Magazine 4 (2022)
DOI: 10.1109/TED.2021.3089995, (KI-IoT)
In-memory computing (IMC) is one of the most promising candidates for data-intensive computing accelerators of machine learning (ML). A key ML algorithm for dimensionality reduction and classification is the principal component analysis (PCA), which heavily relies on matrix-vector multiplications (MVM) for which classic von Neumann architectures are not optimized. Here, we provide the experimental demonstration of a new IMC-based PCA algorithm based on power iteration and deflation executed in a 4-kbit array of resistive switching randomaccess memory (RRAM). The classification accuracy of Wisconsin Breast Cancer dataset reaches 95.43%, close to floating-point implementation. Our simulations indicate a 250 improvement in energy efficiency compared to commercial graphic processing units (GPUs), thus supporting IMC for energy-efficient ML in modern data-intensive computing.

(14) In-Memory Principal Component Analysis by Crosspoint Array of Rresistive Switching Memory
P. Mannocci, A. Baroni, E. Melacarne, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
IEEE Nanotechnology Magazine 4 (2022)
DOI: 10.1109/TED.2021.3089995, (Neutronics)
In-memory computing (IMC) is one of the most promising candidates for data-intensive computing accelerators of machine learning (ML). A key ML algorithm for dimensionality reduction and classification is the principal component analysis (PCA), which heavily relies on matrix-vector multiplications (MVM) for which classic von Neumann architectures are not optimized. Here, we provide the experimental demonstration of a new IMC-based PCA algorithm based on power iteration and deflation executed in a 4-kbit array of resistive switching randomaccess memory (RRAM). The classification accuracy of Wisconsin Breast Cancer dataset reaches 95.43%, close to floating-point implementation. Our simulations indicate a 250 improvement in energy efficiency compared to commercial graphic processing units (GPUs), thus supporting IMC for energy-efficient ML in modern data-intensive computing.

(15) Kafka-ML: Connecting the Data Stream with ML/AI Frameworks
Ch. Martin, P. Langendörfer, P.S. Zarrin, M. Diaz, B. Rubio
Future Generation Computer Systems 126, 15 (2022)
DOI: 10.1016/j.future.2021.07.037
Machine Learning (ML) and Artificial Intelligence (AI) depend on data sources to train, improve, and make predictions through their algorithms. With the digital revolution and current paradigms like the Internet of Things, this information is turning from static data to continuous data streams. However, most of the ML/AI frameworks used nowadays are not fully prepared for this revolution. In this paper, we propose Kafka-ML, a novel and open-source framework that enables the management of ML/AI pipelines through data streams. Kafka-ML provides an accessible and user-friendly Web user interface where users can easily define ML models, to then train, evaluate, and deploy them for inferences. Kafka-ML itself and the components it deploys are fully managed through containerization technologies, which ensure their portability, easy distribution, and other features such as fault-tolerance and high availability. Finally, a novel approach has been introduced to manage and reuse data streams, which may eliminate the need for data storage or file systems.

(16) Modeling and design of an electrically pumped SiGeSn microring laser
B. Marzban, L. Seidel, V. Kiyek, T. Liu, M. H. Zoellner, Z. Ikonic, G. Capellini, D. Buca, J. Schulze, M. Oehme, J. Witzens
Proc. SPIE Photonics West 2022, (2022)
(DFG GeSn Laser)

(17) New Insights Into the Electronic States of the Ge(001) Surface by Joint Angle-Resolved Photoelectron Spectroscopy and First-Principle Calculation Investigation
F. Reichmann, E. Scalise, A.P. Becker, E.V.S. Hofmann, J. Dabrowski, F. Montalenti, L. Miglio, M. Mulazzi, W.M. Klesse, G. Capellini
Applied Surface Science 571, 151264 (2022)
DOI: 10.1016/j.apsusc.2021.151264
While the Ge(001) surface has been extensively studied, it is still debated whether it is of conducting or semiconducting nature at room temperature. The evidence collected by angle-resolved photoelectron spectroscopy experiments in the past has led to the preliminary attribution of a semiconducting nature at room temperature. In contrast, we show in this work that the pristine Ge(001) surface is conducting at room temperature by using temperature-dependent angle-resolved photoelectron spectroscopy, scanning tunneling microscopy and first principles calculations. Specifically, a surface band located ∼200 meV above the valence band maximum has been observed at room temperature. This surface band shows anisotropic dispersions along the [0 1 0] and [1 1 0] directions, but it disappears at lower measurement temperature, which indicates its occupation by thermally excited electrons. State-of-the-art density functional theory calculations undoubtedly attribute this surface band to the unoccupied π*-band formed by dangling bonds on the c(4 × 2) surface reconstruction, while evidencing fundamental differences with the p(2 × 1) reconstruction. Furthermore, the calculations demonstrate that the valence band structure observed in angle-resolved photoelectron spectroscopy experiments arise from projected bulk states and is thus insensitive to surface contamination. Our results contribute to the fundamental knowledge of the Ge(001) surface and to a better understanding of its role in micro- and opto-electronic devices.

(18) AC Electrokinetic Immobilization of Influenza Virus
S. Stanke, Ch. Wenger,, F.F. Bier, R. Hölzel
Electrophoresis 1 (2022)
(BioBic)

(19) Improved Graphene-Base Heterojunction Transistor with Different Collector Semi­conductors for High-Frequency Applications
C. Strobel, C.A. Chavarin, S. Leszczynski, K. Richter, M. Knaut, J. Reif, S. Völkel, M. Albert, Ch. Wenger, J.W. Bartha,T. Mikolajick
Advanced Materials Letters 13(1), 0111688 (2022)
(FFLEXCOM (D020))

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