arXiv subjects

Salman Manzoor

Publications and source records attributed to Salman Manzoor.

3 recordsLinked to original sources

arXiv2023

Hydrogen-induced degradation dynamics in silicon heterojunction solar cells via machine learning

Among silicon-based solar cells, heterojunction cells hold the world efficiency record. However, their market acceptance is hindered by an initial 0.5\% per year degradation of their open circuit voltage which doubles the overall cell degradation rate. Here, we study the performance degradation of crystalline-Si/amorphous-Si:H heterojunction stacks. First, we experimentally measure the interface defect density over a year, the primary driver of the degradation. Second, we develop SolDeg, a multiscale, hierarchical simulator to analyze this degradation by combining Machine Learning, Molecular Dynamics, Density Functional Theory, and Nudged Elastic Band methods with analytical modeling. We discover that the chemical potential for mobile hydrogen develops a gradient, forcing the hydrogen to drift from the interface, leaving behind recombination-active defects. We find quantitative correspondence between the calculated and experimentally determined defect generation dynamics. Finally, we propose a reversed Si-density gradient architecture for the amorphous-Si:H layer that promises to reduce the initial open circuit voltage degradation from 0.5\% per year to 0.1\% per year.

cond-mat.mtrl-sci↗

arXiv2022

ThreatPro: Multi-Layer Threat Analysis in the Cloud

Many effective Threat Analysis (TA) techniques exist that focus on analyzing threats to targeted assets (e.g., components, services). These techniques consider static interconnections among the assets. However, in dynamic environments, such as the Cloud, resources can instantiate, migrate across physical hosts, or decommission to provide rapid resource elasticity to the users. It is evident that existing TA techniques cannot address all these requirements. In addition, there is an increasing number of complex multi-layer/multi-asset attacks on Cloud systems, such as the Equifax data breach. Hence, there is a need for threat analysis approaches that are designed to analyze threats in complex, dynamic, and multi-layer Cloud environments. In this paper, we propose ThreatPro that addresses the analysis of multi-layer attacks and supports dynamic interconnections in the Cloud. ThreatPro facilitates threat analysis by developing a technology-agnostic information flow model, which represents the Cloud's functionality through a set of conditional transitions. The model establishes the basis to capture the multi-layer and dynamic interconnections during the life-cycle of a Virtual Machine (VM). Specifically, ThreatPro contributes in (a) enabling the exploration of a threat's behavior and its propagation across the Cloud, and (b) assessing the security of the Cloud by analyzing the impact of multiple threats across various operational layers/assets. Using public information on threats from the National Vulnerability Database (NVD), we validate ThreatPro's capabilities, i.e., (a) identify and trace actual Cloud attacks and (b) speculatively postulate alternate potential attack paths.

cs.CR↗

arXiv2021

From Femtoseconds to Gigaseconds: The SolDeg Platform for the Performance Degradation Analysis of Silicon Heterojunction Solar Cells

Heterojunction Si solar cells exhibit notable performance degradation. We developed the SolDeg platform to model this degradation as electronic defects getting generated by thermal activation across energy barriers over time. First, molecular dynamics simulations were performed to create a-Si/c-Si stacks, using a machine-learning-based Gaussian approximation potential. Second, we created shocked clusters by a cluster blaster. Third, the shocked clusters were analyzed to identify which of them supported electronic defects. Fourth, the distribution of energy barriers that control the generation of these electronic defects was determined. Fifth, an accelerated Monte Carlo method was developed to simulate the thermally activated time dependent defect generation across the barriers. Our main conclusions are as follows. (1) The degradation of a-Si/c-Si stacks via defect generation is controlled by a broad distribution of energy barriers. (2) We developed the SolDeg platform to track the microscopic dynamics of defect generation across this wide barrier distribution, and determined the time dependent defect density $N(t)$ from femtoseconds to gigaseconds, over 24 orders of magnitude in time. (3) We have shown that a stretched exponential analytical form can successfully describe the defect generation $N(t)$. (4) We found that in relative terms $V_\mathrm{oc}$ degrades at a rate of 0.2%/year over the first year, slowing with advancing time. (5) We developed the Time Correspondence Curve to calibrate and validate the accelerated testing of solar cells. We found a compellingly simple scaling relationship between accelerated and normal times $t_\mathrm{accelerated} \propto t_\mathrm{normal}^{0.85}$. (6) We ourselves carried out experimental studies of defect generation in a-Si:H/c-Si stacks. We found a relatively high degradation rate at early times, that slowed considerably at longer time scales.

cond-mat.mtrl-sci↗