Mila Lewerenz


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Lewerenz

First Name

Mila

ORCID

0000-0002-6715-4982

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2021 - 202613
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Publications1 - 10 of 13
  • Sn Alloying to Improve Memristive Device Performance
    Item type: Other Conference Item
    Passerini, Elias; Lewerenz, Mila; Csontos, Miklos; et al. (2023)
  • Gate-Enabled Operation Point Tuning in an Atomic-Scale Memristor
    Item type: Other Conference Item
    Lewerenz, Mila; Passerini, Elias; Cheng, Bojun; et al. (2021)
  • Reconfigurable artificial neuron and synapse enabled through a single alloyed memristor
    Item type: Journal Article
    Passerini, Elias; Lewerenz, Mila; Schneuwly, Arnaud; et al. (2025)
    Scientific Reports
    Memristive devices have drawn significant interest due to their use in novel paradigms such as neuromorphic computing. Neuromorphic systems are developed by implementing artificial neurons and synapses on a hardware level. Hence, memristors with multipurpose and reconfigurable neuromorphic functionalities could be highly beneficial in the design process. In this study, we experimentally verify that both neuronal and synaptic functions can be implemented on a single memristor. By controlling the device current at two different levels, the memristor operates in either a volatile or a nonvolatile retention regime. These two operation regimes are essential to mimic neuronal or synaptic behavior. Towards this end, we use an alloyed filamentary memristor (AgSn/SiO₂/Pt) composed of ions with differing mobilities enabling both integrate and fire (IF) operation in the volatile regime and synaptic weights in the nonvolatile regime. By only changing the current compliance, these devices switch reliably between the aforementioned retention regimes. Additionally, our proposed training method significantly improves switching variability in the volatile regime. We show how the mean set voltage statistically reduce from 1.2 to 0.2V; and the standard deviation of the set voltages reduced from 0.52 to 0.03V.
  • Confined filament growth in Ag-Nanoparticle Memristor
    Item type: Other Conference Item
    Fischer, Markus; Eglin, David; Lewerenz, Mila; et al. (2025)
    An Ag memristor based on a single Ag nanoparticle was presented. Operating at set voltages as low as 150 mV, it is believed that an ECM switching mechanism leads to the reversible metallization of an electromigration-created gap. The new, self-aligned fabrication process is comparatively simple with a single lithography step and is potentially scalable. The device could find future applications in fields where tunable behaviour or operation characteristics are of great advantage, such as neuromorphic computing, photonic devices or chemical sensing.
  • Controlling Volatility and Nonvolatility of Memristive Devices by Sn Alloying
    Item type: Journal Article
    Passerini, Elias; Lewerenz, Mila; Csontos, Miklos; et al. (2023)
    ACS Applied Electronic Materials
    Memristive devices have attracted significant attention due to their downscaling potential, low power operation, and fast switching performance. Their inherent properties make them suitable for emerging applications such as neuromorphic computing, in-memory computing, and reservoir computing. However, the different applications demand either volatile or nonvolatile operation. In this study, we demonstrate how compliance current and specific material choices can be used to control the volatility and nonvolatility of memristive devices. Especially, by mixing different materials in the active electrode, we gain additional design parameters that allow us to tune the devices for different applications. We found that alloying Ag with Sn stabilizes the nonvolatile retention regime in a reproducible manner. Additionally, our alloying approach improves the reliability, endurance, and uniformity of the devices. We attribute these advances to stabilization of the filament inside the switching medium by the inclusion of Sn in the filament structure. These advantageous properties of alloying were found by investigating a choice of six electrode materials (Ag, Cu, AgCu-1, AgCu-2, AgSn-1, AgSn-2) and three switching layers (SiO2, Al2O3, HfO2).
  • A Three-Terminal Memristive Artificial Neuron with Tunable Firing Probability
    Item type: Journal Article
    Lewerenz, Mila; Passerini, Elias; Weber, Luca; et al. (2024)
    Advanced Electronic Materials
    The human brain facilitates information processing via generating and receiving temporal patterns of short voltage pulses, a.k.a. neural spikes. This approach simultaneously grants low-power operation as well as a high degree of noise immunity and fault tolerance at a small footprint and simplistic structure of the neurons. To date, the latter two key features are critically missing from the toolbox of artificial spiking neural network hardware, hindering the development of scalable and sustainable artificial intelligence (AI) platforms. Here, a compact, gate-tunable neuron circuit is demonstrated, and its potential as a functional leaky integrate-and-fire (LIF) neuron is explored. It relies on a single nanoscale three-terminal (3T) memristor device, which has been downscaled by 30% compared to previous work, where the set voltage and, thereby, the spiking probability of the neuron circuit can be widely tuned by the low-voltage operation of the gate electrode. The influence of the gate voltage on the two-terminal (2T) current-voltage characteristics is measured, statistically analyzed, and further utilized in a custom-built LTspice model. The circuit simulations account for the experimentally observed, adjustable set voltage. The presented results demonstrate the merits of 3T memristors as compact, tunable, and versatile artificial neurons for neuromorphic computing applications.
  • Three-Terminal Memristors: Enabling Dynamic Tunability at the Atomic-Scale
    Item type: Doctoral Thesis
    Lewerenz, Mila (2024)
  • Versatile Nanoscale Three-Terminal Memristive Switch Enabled by Gating
    Item type: Journal Article
    Lewerenz, Mila; Passerini, Elias; Cheng, Bojun; et al. (2024)
    ACS Nano
    A three-terminal memristor with an ultrasmall footprint of only 0.07 mu m(2) and critical dimensions of 70 nm x 10 nm x 6 nm is introduced. The device's feature is the presence of a gate contact, which enables two operation modes: either tuning the set voltage or directly inducing a resistance change. In I-V mode, we demonstrate that by changing the gate voltages between +/- 1 V one can shift the set voltage by 69%. In pulsing mode, we show that resistance change can be triggered by a gate pulse. Furthermore, we tested the device endurance under a 1 kHz operation. In an experiment with 2.6 million voltage pulses, we found two distinct resistance states. The device response to a pseudorandom bit sequence displays an open eye diagram and a success ratio of 97%. Our results suggest that this device concept is a promising candidate for a variety of applications ranging from Internet-of-Things to neuromorphic computing.
  • Integrated Photonic-Electronic Memristors
    Item type: Other Conference Item
    Emboras, Alexandros; Portner, Kevin; Weilenmann, Christoph; et al. (2022)
    OSA Technical Digest ~ Conference on Lasers and Electro-Optics
    We integrate memristors in a silicon photonic/plasmonic platform and demonstrate modulators, photodetectors and electronic devices complemented with memory effect. The demonstrated memristors could be the key photonic building blocks in hybrid photonic-electronic neuromorphic chips.
  • Pulsed Atomic-Scale Three-Terminal Memristor
    Item type: Other Conference Item
    Lewerenz, Mila; Passerini, Elias; Cheng, Bojun; et al. (2023)
Publications1 - 10 of 13