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Defects in the rutile TiO${}_2$ structures have been extensively studied, but the intrinsic defects of the oxygen deficient Ti${}_n$O${}_{2n-1}$ phases have not been given the same amount of consideration. Those structures, known as…
The broadening and optical parameters of TiO2/SiO2 transition layers depending on the ion energy have been investigated using the Rutherford Backscattering Spectrometry (RBS) and Ellipstrometry Spectroscopy (ES) methods. The TiO2/SiO2…
We report on the fabrication and characterization of superconducting coplanar waveguide (CPW) resonators based on titanium nitride (TiN) thin films deposited on 200\,mm diameter high-resistivity Si(100) substrates. We systematically…
Emerging non-volatile memory (NVM), or memristive, devices promise energy-efficient realization of deep learning, when efficiently integrated with mixed-signal integrated circuits on a CMOS substrate. Even though several algorithmic…
Neuromorphic computing, commonly understood as a computing approach built upon neurons, synapses, and their dynamics, as opposed to Boolean gates, is gaining large mindshare due to its direct application in solving current and future…
The structural interface properties of layered Pt/Ti/SiO2/Si electrodes have been investigated using high-resolution specular and diffuse x-ray reflectivity under grazing angles. Currently this multilayer system represents a technological…
In this work we report on the role of ion transport for the dynamic behavior of a double barrier quantum mechanical Al/Al$_2$O$_3$/Nb$_{\text{x}}$O$_{\text{y}}$/Au memristive device based on numerical simulations in conjunction with…
AI-powered autonomous experimentation (AI/AE) can accelerate materials discovery but its effectiveness for electronic materials is hindered by data scarcity from lengthy and complex design-fabricate-test-analyze cycles. Unlike experienced…
On-chip photonic devices based on SiO$_2$ are of interest for applications such as microresonator gyroscopes and microwave sources. Although SiO$_2$ microdisk resonators have achieved quality factors exceeding one billion, this value…
Resistive switching devices emerged a huge amount of interest as promising candidates for non-volatile memories as well as artificial synapses due to their memristive behavior. The main physical and chemical phenomena which define their…
The upcoming sixth Generation (6G) of wireless networks envisions ultra-low latency and energy efficient Edge Inference (EI) for diverse Internet of Things (IoT) applications. However, traditional digital hardware for machine learning is…
High-k oxides such as SrTiO3 promise large capacitance, but their dielectric response is often limited by leakage currents due to reduced bandgaps. We show that introducing a thin barrier layer beneath SrTiO3 is a simple and effective way…
Compact, high-speed electro-optic phase modulators play a vital role in various large-scale applications including phased arrays, quantum and neural networks, and optical communication links. Conventional phase modulators suffer from a…
We report on spin atomistic calculations used to model static and dynamic magnetic properties of inhomogeneous ultrathin iron films. Active magnetic layers in next-generation spintronic devices are becoming so thin that they exhibit some…
The unprecedented dissemination of edge devices is accompanied by a growing demand for neuromorphic chips that can process time-series data natively without cloud support. Echo state network (ESN) is a class of recurrent neural networks…
Spin-based silicon quantum dots are an attractive qubit technology for quantum information processing with respect to coherence time, control, and engineering. Here we present an exchange-only Si qubit device platform that combines the…
The electrically readable complex dynamics of robust and scalable magnetic tunnel junctions (MTJs) offer promising opportunities for advancing neuromorphic computing. In this work, we present an MTJ design with a free layer and two…
Deep learning has made remarkable progress in various tasks, surpassing human performance in some cases. However, one drawback of neural networks is catastrophic forgetting, where a network trained on one task forgets the solution when…
Reservoir computing is a highly efficient machine learning framework for processing temporal data by extracting features from the input signal and mapping them into higher dimensional spaces. Physical reservoir layers have been realized…
In recent years, new methods for solving partial differential equations (PDEs) such as Monte Carlo random walk methods have gained considerable attention. However, due to the lack of hardware-intrinsic randomness in the conventional von…