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I present a review of both experimental and theoretical studies performed during the recent three years, which deal with the physical properties and possible applications of graphene placed on ferroelectric (organic or Pb(ZrxTi1-x)O3 (PZT)…
We report a detailed experimental and theoretical investigation of the effect of residual strain, and strain relaxation, which manifests itself at the Si/SiO$_{2}$ interfaces in commercial silicon-on-insulator (SOI) wafers. SOI material is…
Nanostructures, especially of silicon, are of paramount importance for new generation solar cell applications. The key material requirements for solar cells are good electrical conductivity, confinement of excitons, and a tunable band-gap…
Topologically trivial insulators can be classified into atomic insulators (AIs) and obstructed atomic insulators (OAIs) depending on whether the Wannier charge centers are localized or not at spatial positions occupied by atoms. An OAI can…
Synaptic devices with linear high-speed switching can accelerate learning in artificial neural networks (ANNs) embodied in hardware. Conventional resistive memories however suffer from high write noise and asymmetric conductance tuning,…
We have studied resistive bistability (memory) effects in junctions based on metal oxides, with a focus on sample-to-sample reproducibility which is necessary for the use of such junctions as crosspoint devices of hybrid CMOS/nanoelectronic…
Memory devices based on resistive switching (RS) have not been fully realised due to lack of understanding of the underlying switching mechanisms. Nature of ion transport responsible for switching and growth of conducting filament in…
The Mixture-of-Experts (MoE) models have emerged as the state-of-the-art paradigm for scaling up large language models (LLMs) without proportionally increased computational cost. However, its on-device deployment faces a critical challenge…
Spin-torque memristors were proposed in 2009, which could provide fast, low-power and infinite memristive behavior for large-density non-volatile memory and neuromorphic computing. However, the strict requirements of combining high…
Memory effects can have a profound impact on the resistivity of semiconductor systems, resulting in giant negative magnetoresistance and MIRO phenomena. This work opens the discussion of the memory effects in 3D conducting systems featured…
Scaling down materials to an atomic-layer level produces rich physical and chemical properties as exemplified in various two-dimensional (2D) crystals extending from graphene, transition metal dichalcogenides to black phosphorous. This is…
Silicon anodes offer high energy densities for next-generation lithium-ion batteries; however, their application is limited by severe volume expansion during cycling. Making silicon porous or nanostructured mitigates this expansion but…
In this paper, we report on the charge transport properties of hybrid nanoparticles (NPs) based on the polymeric 1D compound [Fe(Htrz)2(trz)](BF4), which is one of the most promising SCO systems for designing electronic devices. In…
We demonstrate that the electronic structure of mesoporous silicon is affected by adsorption of nitro-based explosive molecules in a compound-selective manner. This selective response is demonstrated by probing the adsorption of two…
Recently, chirality-induced spin selectivity (CISS) has been observed in chiral molecules and is attractive for application in magnetoresistance (MR) devices. In this study, we fabricate CISS-based nanodevices consisting of chiral molecules…
Ferroelectrics offer a promising materials platform to realize energy-efficient non-volatile memory technology with the FeFET-based implementations being one of the most area-efficient ferroelectric memory architectures. However, the FeFET…
The rapid growth of digital technology has driven the need for efficient storage solutions, positioning memristors as promising candidates for next-generation non-volatile memory (NVM) due to their superior electrical properties. Organic…
Memristor-based neuromorphic computing could overcome the limitations of traditional von Neumann computing architectures -- in which data are shuffled between separate memory and processing units -- and improve the performance of deep…
Non-volatile memory arrays can deploy pre-trained neural network models for edge inference. However, these systems are affected by device-level noise and retention issues. Here, we examine damage caused by these effects, introduce a…
By means of molecular dynamics simulations based on the Billeter et al. [S. R. Billeter, A. Curioni, D. Fischer, and W. Andreoni, Phys. Rev. B {\bf 73}, 155329] environment-dependent classical force field we studied the structural features…