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Hyperbolic crystals like ��-MoO$_3$ can support large wavevectors and photon density as compared to the commonly used dielectric crystals, which makes them a highly desirable platform for compact photonic devices. The extreme anisotropy of the dielectric constant in these crystals is intricately linked with the anisotropic character of the phonons, which along with photon confinement leads to the rich physics of phonon polaritons. However, the chiral nature of phonons in these hyperbolic crystals have not been studied in detail. In this study, we report our observations of helicity selective Raman scattering from flakes of ��-MoO$_3$. Both helicity-preserving and helicity-reversing Raman scattering are observed. We observe that helical selectivity is largely governed by the underlying crystal symmetry. This study shed light on the chiral character of the high symmetry phonons in these hyperbolic crystals. It paves the way for exploiting proposed schemes of coupling chiral phonon modes into propagating surface plasmon polaritons and for compact photonic circuits based on helical polarized light., The following article has been submitted to Applied Physics Letters

Memristors have demonstrated immense potential as building blocks in future adaptive neuromorphic architectures. Recently, there has been focus on emulating specific synaptic functions of the mammalian nervous system by either tailoring the functional oxides or engineering the external programming hardware. However, high device-to-device variability in memristors induced by the electroforming process and complicated programming hardware are among the key challenges that hinder achieving biomimetic neuromorphic networks. Here, an electroforming-free and complementary metal oxide semiconductor (CMOS)-compatible memristor based on oxygen-deficient SrTiO$_{3-x}$ (STO$_x$) is reported to imitate synaptic learning rules. Through spectroscopic and cross-sectional transmission electron microscopic analyses, electroforming-free characteristics are attributed to the bandgap reduction of STO$_x$ by the formation of oxygen vacancies. The potential of such memristors to behave as artificial synapses is demonstrated by successfully implementing high order time- and rate-dependent synaptic learning rules. Also, a simple hybrid CMOS-memristor approach is presented to implement a variety of synaptic learning rules. Results are benchmarked against biological measurements form hippocampal and visual cortices with good agreement. This demonstration is a step towards the realization of large scale adaptive neuromorphic computation and networks., Comment: 44 pages, 11 figures

Functional oxides based resistive memories are recognized as potential candidate for the next-generation high density data storage and neuromorphic applications. Fundamental understanding of the compositional changes in the functional oxides is required to tune the resistive switching characteristics for enhanced memory performance. Herein, we present the micro/nano-structural and compositional changes induced in a resistive oxide memory during resistive switching. Oxygen deficient amorphous chromium doped strontium titanate (Cr:$a$-SrTiO$_{3-x}$) based resistance change memories are fabricated in a Ti/Cr:$a$-SrTiO$_{3-x}$ heterostructure and subjected to different biasing conditions to set memory states. Transmission electron microscope based cross-sectional analyses of the memory devices in different memory states shows that the micro/nano-structural changes in amorphous complex oxide and associated redox processes define the resistive switching behavior. These experimental results provide insights and supporting material for Ref. [1]., Comment: 7 pages, 6 figures

Recent advances in predictive data analytics and ever growing digitalization and connectivity with explosive expansions in industrial and consumer Internet-of-Things (IoT) has raised significant concerns about security of people's identities and data. It has created close to ideal environment for adversaries in terms of the amount of data that could be used for modeling and also greater accessibility for side-channel analysis of security primitives and random number generators. Random number generators (RNGs) are at the core of most security applications. Therefore, a secure and trustworthy source of randomness is required to be found. Here, we present a differential circuit for harvesting one of the most stochastic phenomenon in solid-state physics, random telegraphic noise (RTN), that is designed to demonstrate significantly lower sensitivities to other sources of noises, radiation and temperature fluctuations. We use RTN in amorphous SrTiO3-based resistive memories to evaluate the proposed true random number generator (TRNG). Successful evaluation on conventional true randomness tests (NIST tests) has been shown. Robustness against using predictive machine learning and side-channel attacks have also been demonstrated in comparison with non-differential readouts methods.