arXiv subjects

Mario Lanza

Publications and source records attributed to Mario Lanza.

14 recordsLinked to original sources

arXiv2026

Polymer-free van der Waals assembly of 2D material heterostructures using muscovite crystals

The advent of van der Waals (vdW) heterostructures has enabled formation of bespoke materials with atomic precision, where numerous quantum and topological phenomena have already been discovered. This atomic-layer tunability, however, comes at a cost: individual 2D layers must be picked up, moved, and placed in a deterministic manner while keeping their interfaces atomically clean. Recent advances in machine learning and robotics place even stronger emphasis on the deterministic aspect of vdW assembly. Current polymer-based transfer methods satisfy neither the determinism nor cleanliness requirements. To this end, solutions are needed where adhesion can be dynamically and deterministically controlled without leaving organic contamination. Here, we present a polymer free transfer technique employing thin muscovite (mica) crystals. Temperature control over mica adhesion enables deterministic pick-up, stacking, and release of 2D materials, while their crystalline, inorganic nature ensures pristine interfaces and suppresses strain. Fully compatible with existing fabrication workflows, this approach enables the assembly of demanding vdW heterostructures, including those with exposed conductive layers, moir\'e superlattices and suspended membranes. Our method represents a promising strategy for vdW heterostructure fabrication toward its automatization.

cond-mat.mes-hall↗

arXiv2025

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions

Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-looking view of AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing. Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific workflows that connect simulations to experiments and generative systems grounded in synthesisability rather than purely idealised phases. Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation while maintaining reproducibility and physical interpretability. Taken together, these perspectives outline where AI-enabled science stands today, identify bottlenecks in data, methods and infrastructure, and chart concrete directions for building AI systems that are not only more powerful but also more transparent and capable of accelerating discovery in complex real-world environments.

physics.soc-ph↗

arXiv2025

How to Identify Suitable Gate Dielectrics for Transistors based on Two-Dimensional Semiconductors

The recent progress in nanosheet transistors has established two-dimensional (2D) semiconductors as viable candidates for future ultra-scaled electronic devices. Next to reducing contact resistance, identifying good gate dielectrics is a fundamental challenge, as the dielectric/channel interface dramatically impacts virtually all performance parameters. While several promising gate dielectrics have recently been reported, the evaluation of their quality and suitability is often fragmentary and focused on selected important performance metrics of the gate stack, such as the capacitive gate control, leakage currents, reliability, and ease of fabrication and integration. However, identifying a suitable gate stack is a complex problem that has not yet been approached systematically. In this perspective, we aim to formulate general criteria for good gate dielectrics.

physics.app-ph↗

arXiv2024

Modeling the variability of memristive devices with hexagonal boron nitride as dielectric

Variability in memristive devices based on h-BN dielectrics is studied in depth. Different numerical techniques to extract the reset voltage are described and the corresponding cycle-to-cycle variability is characterized by means of the coefficient of variance. The charge-flux domain was employed to develop one of the extraction techniques, the calculation of the integrals of current and voltage to obtain the charge and flux allows to minimize the effects of electric noise and the inherent stochasticity of resistive switching on the measurement data. A model to reproduce charge versus flux curves has been successfully employed. The device variability is also described by means of the time series analysis to assess the memory effect along a resistive switching series. Finally, we analyzed I-V curves under ramped voltage stress utilizing a simulator based on circuit breakers, the formation and rupture of the percolation paths that constitute the conductive nanofilaments is studied to describe the set and reset processes behind the resistive switching operation.

cond-mat.mes-hall↗

arXiv2024

Variability in Resistive Memories

Resistive memories are outstanding electron devices that have displayed a large potential in a plethora of applications such as nonvolatile data storage, neuromorphic computing, hardware cryptography, etc. Their fabrication control and performance have been notably improved in the last few years to cope with the requirements of massive industrial production. However, the most important hurdle to progress in their development is the so-called cycle-to-cycle variability, which is inherently rooted in the resistive switching mechanism behind the operational principle of these devices. In order to achieve the whole picture, variability must be assessed from different viewpoints going from the experimental characterization to the adequation of modeling and simulation techniques. Herein, special emphasis is put on the modeling part because the accurate representation of the phenomenon is critical for circuit designers. In this respect, a number of approaches are used to the date: stochastic, behavioral, mesoscopic..., each of them covering particular aspects of the electron and ion transport mechanisms occurring within the switching material. These subjects are dealt with in this review, with the aim of presenting the most recent advancements in the treatment of variability in resistive memories.

physics.app-ph↗

arXiv2024

Roadmap to Neuromorphic Computing with Emerging Technologies

The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement.

eess.SP↗

arXiv2024

Amorphous Boron Nitride as an Ultrathin Copper Diffusion Barrier for Advanced Interconnects

This study focuses on amorphous boron nitride ($\rm \alpha$-BN) as a novel diffusion barrier for advanced semiconductor technology, particularly addressing the critical challenge of copper diffusion in back-end-of-line (BEOL) interconnects. Owing to its ultralow dielectric constant and robust barrier properties, $\rm \alpha$-BN is examined as an alternative to conventional low-k dielectrics. The investigation primarily employs theoretical modelling, using a Gaussian Approximation Potential, to simulate and understand the atomic-level interactions. This machine learning-based approach allows the performance of realistic simulations of amorphous structure of $\rm \alpha$-BN, enabling the exploration of the impact of different film morphologies on barrier efficacy. Furthermore, we studied the electronic and optical properties of the films using a simple Tight-Binding model. In addition to the theoretical studies, we performed diffusion studies of copper through PECVD $\rm \alpha$-BN on Si. The results from both the theoretical and experimental investigations highlight the potential of $\rm \alpha$-BN as a highly effective diffusion barrier, suitable for integration in nanoelectronics. This research shows that $\rm \alpha$-BN is a promising candidate for BEOL interconnects but also demonstrates the synergy of advanced computational models and experimental methods in material innovation for semiconductor applications.

cond-mat.mtrl-sci↗

arXiv2023

Impact of Hydrogenation on the Stability and Mechanical Properties of Amorphous Boron Nitride

Interconnect materials with ultralow dielectric constant, and good thermal and mechanical properties are crucial for the further miniaturization of electronic devices. Recently, it has been demonstrated that ultrathin amorphous boron nitride (aBN) films have a very low dielectric constant, high density (above 2.1 g/cm3), high thermal stability, and mechanical properties. The excellent properties of aBN derive from the nature and degree of disorder, which can be controlled at fabrication, allowing tuning of the physical properties for desired applications. Here, we report an improvement in the stability and mechanical properties of amorphous boron nitride upon hydrogen doping. With the introduction of a Gaussian approximation potential (GAP) for atomistic simulations, we investigate the changing morphology of amorphous boron nitride with varying H doping concentrations. We found that for 8 at% of H doping, the concentration of $sp^3$-hybridized atoms reaches a maximum which leads to an improvement of thermal stability and mechanical properties by 20%. These results will be a guideline for experimentalists and process engineers to tune the growth conditions of amorphous boron nitride films for numerous applications.

cond-mat.mtrl-sci↗

arXiv2022

Skyrmion-based Leaky Integrate and Fire Neurons for Neuromorphic Applications

Spintronics is an important emerging technology for data storage and computation. In this field, magnetic skyrmion-based devices are attractive due to their small size and energy consumption. However, controlling the creation, deletion and motion of skyrmions is challenging. Here we propose a novel energy-efficient skyrmion-based device structure, and demonstrate its use as leaky integrate (LIF) and fire neuron for neuromorphic computing. Here we show that skyrmions can be confined by patterning the geometry of the free layer in a magnetic tunnel junction (MTJ), and demonstrate that the size of the skyrmion can be adjusted by applying pulsed voltage stresses. A spiking neural network (SNN) made of such skyrmion-based LIF neurons shows the capability of classifying images from the Modified National Institute of Standards and Technology (MNIST) dataset.

cond-mat.mtrl-sci↗

arXiv2021

2022 Roadmap on Neuromorphic Computing and Engineering

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges. We hope that this Roadmap will be a useful resource to readers outside this field, for those who are just entering the field, and for those who are well established in the neuromorphic community. https://doi.org/10.1088/2634-4386/ac4a83

cs.ET↗

arXiv2020

Inkjet printed circuits with two-dimensional semiconductor inks for high-performance electronics

Air-stable semiconducting inks suitable for complementary logic are key to create low-power printed integrated circuits (ICs). High-performance printable electronic inks with two-dimensional materials have the potential to enable the next generation of high performance, low-cost printed digital electronics. Here we demonstrate air-stable, low voltage (< 5 V) operation of inkjet-printed n-type molybdenum disulfide (MoS2) and p-type indacenodithiophene-co-benzothiadiazole (IDT-BT) field-effect transistors (FETs), estimating a switching time of {\tau} ~ 3.3 {\mu}s for the MoS2 FETs. We achieve this by engineering high-quality MoS2 and air-stable IDT-BT inks suitable for inkjet-printing complementary pairs of n-type MoS2 and p-type IDT-BT FETs. We then integrate MoS2 and IDT-BT FETs to realise inkjet-printed complementary logic inverters with a voltage gain |Av| ~ 4 when in resistive load configuration and |Av| ~ 1.36 in complementary configuration. These results represent a key enabling step towards ubiquitous long-term stable, low-cost printed digital ICs.

cond-mat.mes-hall↗

arXiv2020

On the suitability of hBN as an insulator for 2D material-based ultrascaled CMOS devices

Complementary metal oxide semiconductor (CMOS) logic circuits at the ultimate scaling limit place the utmost demands on the properties of all materials involved. The requirements for semiconductors are well explored and could possibly be satisfied by a number of layered two-dimensional (2D) materials, like for example transition-metal dichalcogenides or black phosphorus. The requirements for the gate insulator are arguably even more challenging and difficult to meet. In particular the combination of insulator to semiconductor which forms the central element of the metal oxide semiconductor field effect transistor (MOSFET) has to be of superior quality in order to build competitive devices. At the moment, hexagonal boron nitride (hBN) is the most common two-dimensional insulator and widely considered to be the most promising gate insulator in nanoscaled 2D material-based transistors. Here, we critically assess the material parameters of hBN and conclude that while its properties render hBN an ideal candidate for many applications in 2D nanoelectronics, hBN is most likely not suitable as a gate insulator for ultrascaled CMOS devices.

physics.app-ph↗

arXiv2019

Visualization of Ion Channels in Membranes using Electrochemical Strain Microscopy

Polymer composite electrolytes of Nafion and phosphotungstic acid (PWA) are fabricated and analyzed using electrochemical strain microscopy (ESM) and conductive atomic force microscopy (C-AFM) to visualize hydrophilic ion channels near the surface, which are composed of water and sulfonic acid groups. The results indicate that the fibrillar objects in ESM image, without significant changes in topography, are hydrophilic ion channels and additional ion channels formed by interaction between PWA and sulfonic groups in Nafion. In this study, the buried ion channels lying under the surface are probed as well as the inlet and outlet of the channels on the surface through combined use of ESM and C-AFM. The results further enhance the understanding of ionic conduction in composite polymer electrolytes in various fields.

physics.app-ph↗

arXiv2018

Electrical homogeneity of large-area chemical vapor deposited multilayer hexagonal boron nitride sheets

Hexagonal boron nitride (h-BN) is a two dimensional (2D) layered insulator with superior dielectric performance that offers excellent interaction with other 2D materials (e.g. graphene, MoS2). Large-area h-BN can be readily grown on metallic substrates via chemical vapor deposition (CVD), but the impact of local inhomogeneities on the electrical properties of the h-BN and their effect in electronic devices is unknown. Here it is shown that the electrical properties of h-BN stacks grown on polycrystalline Pt vary a lot depending on the crystalline orientation of the Pt grain, but within the same grain the electrical properties of the h-BN are very homogeneous. The reason is that the thickness of the CVD-grown h-BN stack is different on each Pt grain. Conductive atomic force microscopy (CAFM) maps show that the tunneling current across the h-BN stack fluctuates up to 3 orders of magnitude from one Pt grain to another. However, probe station experiments reveal that the variability of electronic devices fabricated within the same Pt grain is surprisingly small. As cutting-edge electronic devices are ultra-scaled, and as the size of the metallic substrate grains can easily exceed 100 {\mu}m (in diameter), CVD-grown h-BN stacks may be useful to fabricate electronic devices with low variability.

cond-mat.mtrl-sci↗