arXiv subjects

Shalini Tripathi

Publications and source records attributed to Shalini Tripathi.

7 recordsLinked to original sources

arXiv2026

Process-Technology Co-optimization for 2D-FETs

We present the first experimental machine learning (ML)-enabled Process-Technology Co-Optimization (PTCO) framework for optimizing 2D transition metal dichalcogenide (TMD) FET fabrication directly from statistically meaningful experimental data rather than pure simulation data. We first introduce a transition voltage metric, VTrans, to quantify the gate voltage required for off-to-on switching and reveal its direct correlation with subthreshold swing (SS), highlighting an overlooked switching characteristic that governs both off-state and on-state performance. By integrating automated metric extraction, multi-objective recipe ranking, and predictive modeling, our framework uncovers hidden process-performance correlations and predicts the performance of unexplored fabrication recipes from limited experimental data. Experimental validation shows close agreement with ML predictions, thus demonstrating the framework's ability to efficiently guide gate stack optimization through iterative experimental feedback.

cond-mat.mes-hall↗

arXiv2026

Seed Layer Engineering for Effective Charge Transfer Doping of MoS$_2$ Transistors

Integrating two-dimensional semiconductors such as MoS$_2$ with dielectric materials remains a central challenge for their use in future logic technologies. While seed layers are typically introduced to promote dielectric nucleation and adhesion, we show that they also critically govern charge-transfer doping and, in turn, transistor performance. Back-gated monolayer MoS$_2$ transistors passivated on their top-surface with a Ta-seed/HfO$_x$ dielectric stack were fabricated and characterized electrically and physically using Raman, photoluminescence, and X-ray photoelectron spectroscopies. Threshold voltage and on-current varied strongly with Ta-seed thickness and deposition conditions, and these changes correlated with signatures observed across all spectroscopic probes. The results reveal that the seed layer both introduces disorder into the MoS$_2$ channel and modifies the interfacial charge environment controlling charge transfer between HfO$_x$ and MoS$_2$. Optical spectroscopy shows that on-current tracks seed-induced disorder, whereas X-ray photoelectron spectroscopy indicates that threshold voltage correlates with shifts in the local electrostatic environment associated with interfacial charge transfer. Better performance was obtained with ultrathin 0.2 nm Ta seed layers deposited under oxygen-poor conditions, which limit deposition-induced damage while facilitating charge transfer. These findings identify seed-layer engineering as a key strategy for controlling disorder and interfacial doping in MoS$_2$ devices and establish multimodal spectroscopy as a practical during-fabrication approach for process development and monitoring.

cond-mat.mtrl-sci↗

arXiv2026

Reinforcement Learning for Secrecy Optimization in Underwater Energy Harvesting Relay Network

This paper explores secure communication in an underwater energy-harvesting (EH) relay network that supports hybrid optical-acoustic transmission. The optical hop is modeled using a Gamma-Gamma turbulence channel with pointing errors and may occasionally be blocked by underwater obstacles. At the same time, an eavesdropper is assumed to monitor the acoustic hop, creating a secrecy concern. To address this, we formulate the relay power allocation problem as an infinite-horizon Markov decision process (MDP). A model-based reinforcement learning (RL) driven optimal power allocation (OPA) strategy is proposed to maximize long-term cumulative secrecy performance until the network stops functioning. To offer lower-complexity alternatives, we also develop a Greedy Algorithm (GA) and a Naive Algorithm (NA). Simulation results show that the RL based OPA adapts effectively to battery dynamics, varying channel conditions, and optical link availability, achieving the highest secure data transmission, while GA performs reasonably and NA performs poorly due to its short-sighted decisions.

eess.SP↗

arXiv2026

Secure Energy Efficient Wireless Transmission: A Finite v/s Infinite-Horizon RL Solution

In this paper, a joint optimal allocation of transmit power at the source and jamming power at the destination is proposed to maximize the average secrecy energy efficiency (SEE) of a wireless network within a finite time duration. The destination transmits the jamming signal to improve secrecy by utilizing full-duplex capability. The source and destination both have energy harvesting (EH) capability with limited battery capacity. Due to the Markov nature of the system, the problem is formulated as a finite-horizon reinforcement learning (RL) problem. We propose the finite-horizon joint power allocation (FHJPA) algorithm for the finite-horizon RL problem and compare it with a low-complexity greedy algorithm (GA). An infinite-horizon joint power allocation (IHJPA) algorithm is also proposed for the corresponding infinite-horizon problem. A comparative analysis of these algorithms is carried out in terms of SEE, expected total transmitted secure bits, and computational complexity. The results show that the FHJPA algorithm outperforms the GA and IHJPA algorithms due to its appropriate modelling in finite horizon transmission. When the source node battery has sufficient energy, the GA can yield performance close to the FHJPA algorithm despite its low-complexity. When the transmission time horizon increases, the accuracy of the infinite-horizon model improves, resulting in a reduced performance gap between FHJPA and IHJPA algorithms. The computational time comparison shows that the FHJPA algorithm takes $16.6$ percent less time than the IHJPA algorithm.

eess.SP↗

arXiv2025

Joint Transmit and Jamming Power Optimization for Secrecy in Energy Harvesting Networks: A Reinforcement Learning Approach

In this paper, we address the problem of joint allocation of transmit and jamming power at the source and destination, respectively, to enhance the long-term cumulative secrecy performance of an energy-harvesting wireless communication system until it stops functioning in the presence of an eavesdropper. The source and destination have energy-harvesting devices with limited battery capacities. The destination also has a full-duplex transceiver to transmit jamming signals for secrecy. We frame the problem as an infinite-horizon Markov decision process (MDP) problem and propose a reinforcement learning (RL)-based optimal joint power allocation (OJPA) algorithm that employs a policy iteration (PI) algorithm. Since the optimal algorithm is computationally expensive, we develop a low-complexity sub-optimal joint power allocation (SJPA) algorithm, namely, reduced state joint power allocation (RSJPA). Two other SJPA algorithms, the greedy algorithm (GA), and the naive algorithm (NA) are implemented as benchmarks. In addition, the OJPA algorithm outperforms the individual power allocation (IPA) algorithms termed individual transmit power allocation (ITPA) and individual jamming power allocation (IJPA), where the transmit and jamming powers, respectively, are optimized individually. The results show that the OJPA algorithm is also more energy efficient.Results also show that the OJPA algorithm significantly improves the secrecy performance compared to all SJPA algorithms. The OJPA algorithm also outperforms the secrecy performance of a genetic algorithm-based RL algorithm and a finite-horizon RL algorithm.The proposed RSJPA algorithm achieves nearly optimal performance with significantly less computational complexity marking it the balanced choice between the complexity and the performance.

eess.SP↗

arXiv2021

Recurrent Neural Network Assisted Transmitter Selection for Secrecy in Cognitive Radio Network

In this paper, we apply the long short-term memory (LSTM), an advanced recurrent neural network based machine learning (ML) technique, to the problem of transmitter selection (TS) for secrecy in an underlay small-cell cognitive radio network with unreliable backhaul connections. The cognitive communication scenario under consideration has a secondary small-cell network that shares the same spectrum of the primary network with an agreement to always maintain a desired outage probability constraint in the primary network. Due to the interference from the secondary transmitter common to all primary transmissions, the secrecy rates for the different transmitters are correlated. LSTM exploits this correlation and matches the performance of the conventional technique when the number of transmitters is small. As the number grows, the performance degrades in the same manner as other ML techniques such as support vector machine, $k$-nearest neighbors, naive Bayes, and deep neural network. However, LSTM still significantly outperforms these techniques in misclassification ratio and secrecy outage probability. It also reduces the feedback overhead against conventional TS.

eess.SP↗

arXiv2020

Chemical segregation in Ge2Sb2Te5 thin films during in-situ heating

Germanium antimony telluride has been the most used and studied phase-change material for electronic memory due to its suitable crystallization temperature, amorphous to crystalline resistance contrast, and stability of the amorphous phase. In this work, the segregation of Ge in a Ge2Sb2Te5 film of 30 nm thickness during heating inside the transmission electron microscope was observed and characterized. The Ge2Sb2Te5 film was deposited using sputtering on a Protochips Fusion holder and left uncapped in atmosphere for about four months. Oxygen incorporated within the film played a significant role in the chemical segregation observed which resulted in amorphous Ge-O grain boundaries and Sb and Te rich crystalline domains. Such composition changes can occur when the phase-change material interfaces insulating oxide layers in an integrated device and would significantly impact its electrical and thermal properties.

cond-mat.mtrl-sci↗