arXiv subjects

Shruti Verma

Publications and source records attributed to Shruti Verma.

3 recordsLinked to original sources

arXiv2026

Fast reduction of electron-beam-activated graphene oxide by an infrared laser pulse

Rapid and controllable reduction of graphene oxide (GO) remains a critical challenge for realizing its full technological potential. Here, we report efficient reduction of GO by a synergistic electron-beam-assisted single-pulse near-infrared (NIR) laser process. Time-resolved electron energy-loss spectroscopy measured with a dynamic transmission electron microscope (DTEM) is used to locally track the oxygen concentration evolution after NIR laser pulse irradiation. This finds an oxygen diffusivity of 1.6 +/- 0.4 x 10$^{-8}$ m$^2$/s, which corresponds to 90% reduction of a 46-nm thick film within 960 ns. Electron beam irradiation is found to change the optical absorptivity of GO in the NIR region and the thermal heating cycle resulting from the laser pulse is simulated. Structural characterization via selected-area electron diffraction (SAED) and high-resolution transmission electron microscopy (HRTEM) finds localized restoration of sp$^2$ bonding accompanied by turbostatic disorder in the reduced GO. Together, these results point to a mechanism involving the creation of defects and vacancies produced by electron beam irradiation, which increases the efficiency of NIR light absorption and oxygen diffusion normal to the layers. This study demonstrates the important role of such defects in controlling the photochemistry of GO and its response to NIR illumination.

physics.app-ph↗

arXiv2026

Tracking visible pulsed laser annealing of Hf$_{0.5}$Zr$_{0.5}$O$_2$ heterostructures with in situ transmission electron microscopy

Laser annealing offers a promising route to back end of the line fabrication of ferroelectric thin film transistors based on hafnium-zirconium oxide (HZO). Due to the wide band gap of this material, previous reports have studied the crystallization of HZO using ultraviolet or infrared light. In contrast, we monitor its crystallization in a Si$_3$N$_4$/TiN/Hf$_{0.5}$Zr$_{0.5}$O$_2$ thin film heterostructure upon irradiation with visible nanosecond laser pulses. This geometry mimics the structure of CMOS devices and harnesses the absorption of TiN in the visible regime to generate the heat necessary for the transformation. Through a series of local in situ measurements using a modified transmission electron microscope, we quantify the relationship between the HZO film thickness, critical laser energy density and the ferroelectric HZO phase fraction, finding a sharp threshold behavior in the laser pulse energy necessary to crystallize HZO. The optimal condition of irradiating an 8-nm HZO film with a single laser pulse with an energy density of 177 mJ/cm$^2$ is found to produce 86% of the ferroelectric orthorhombic phase. Heat transfer dynamics within the heterostructure during laser annealing are revealed by finite element simulations, where the partial melting of the silicon nitride substrate is found to play an important role limiting the temperature to 1900 {\deg}C. This finding as well as the observed laser pulse energy threshold behavior support a kinetic crystallization pathway involving the tetragonal phase. More generally, these findings show how laser-driven phase engineering can lead to scalable design and enhanced performance of ferroelectric materials in advanced electronic applications.

cond-mat.mtrl-sci↗

arXiv2020

Chrome Dino Run using Reinforcement Learning

Reinforcement Learning is one of the most advanced set of algorithms known to mankind which can compete in games and perform at par or even better than humans. In this paper we study most popular model free reinforcement learning algorithms along with convolutional neural network to train the agent for playing the game of Chrome Dino Run. We have used two of the popular temporal difference approaches namely Deep Q-Learning, and Expected SARSA and also implemented Double DQN model to train the agent and finally compare the scores with respect to the episodes and convergence of algorithms with respect to timesteps.

cs.AI↗