Elias Passerini


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Passerini

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Elias

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Publications1 - 10 of 14
  • Passerini, Elias; Lewerenz, Mila; Csontos, Miklos; et al. (2023)
  • Cheng, Bojun; Emboras, Alexandros; Passerini, Elias; et al. (2021)
    IEEE Transactions on Electron Devices
    In this article, we present ultralow leakage logic circuits by combining 3-D memristors with CMOS transistors. Significant leakage current reductions of up to 99% are found by experiments and simulation for a memristive hybrid-inverter if compared with a conventional inverter. Likewise, circuit simulations of memristive hybrid ring oscillators, NAND, or full adders show more than 100% gain in energy efficiency per cycle over state-of-the-art circuits. Importantly, the memristive circuits offer hysteresis-free operation. The hysteresis-free operation is due to properly engineered properties—such as the threshold voltage—of the memristors to match the circuit, as well as the self-adaptive filament diameter of our memristor during operation. Lastly, the memristors feature a 10 8 ON– OFF ratio, enabling both high speed and low leakage (~10 fA) when integrated with a transistor. They also come with a well-controlled filament formation on a ~10-nm footprint, making them ideal to integrate with modern CMOS technology transistors.
  • Koepfli, Stefan M.; Baumann, Michael; Koyaz, Yesim; et al. (2023)
    Science
    Although graphene has met many of its initially predicted optoelectronic, thermal, and mechanical properties, photodetectors with large spectral bandwidths and extremely high frequency responses remain outstanding. In this work, we demonstrate a >500 gigahertz, flat-frequency response, graphene-based photodetector that operates under ambient conditions across a 200-nanometer-wide spectral band with center wavelengths adaptable from <1400 to >4200 nanometers. Our detector combines graphene with metamaterial perfect absorbers with direct illumination from a single-mode fiber, which breaks with the conventional miniaturization of photodetectors on an integrated photonic platform. This design allows for much higher optical powers while still allowing record-high bandwidths and data rates. Our results demonstrate that graphene photodetectors can outperform conventional technologies in terms of speed, bandwidth, and operation across a large spectral range.
  • Lewerenz, Mila; Passerini, Elias; Cheng, Bojun; et al. (2021)
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
Publications1 - 10 of 14