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Experimental demonstration of SnO_(2) nanofiber-based memristors and their data-driven modeling for nanoelectronic applications

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Chip 2023年第4期2卷 142-153页

作者： Soumi Saha Madadi Chetan Kodand Reddy Tati Sai Nikhil Kaushik Burugupally sanghamitra debroy Akshay Salimath Venkat Mattela Surya Shankar Dan Parikshit Sahatiya Department of Electrical&Electronics Engineering Birla Institute of Technology and Science PilaniHyderabad CampusHyderabad 500078India Department of Computer Science and Information Systems Birla Institute of Technology and Science PilaniHyderabad CampusHyderabad 500078India Nanomagnetic Division Ceremorphic India Pvt LtdIndia Ceremorphic Inc.San JoseCA 95131USA

This paper demonstrated the fabrication,characterization,datadriven modeling,and practical application of a 1D SnO_(2)nanofiber-based memristor,in which a 1D SnO_(2)active layer wassandwiched between silver(Ag)and aluminum(Al)electrodes.Thisdevice yielded a very high ROFF:RON of~104(ION:IOFF of~105)with an excellent activation slope of 10 mV/dec,low set voltage ofVSET~1.14 V and good repeatability.This paper physically explained the conduction mechanism in the layered SnO_(2)nanofiber-basedmemristor.The conductive network was composed of nanofibersthat play a vital role in the memristive action,since more conductive paths could facilitate the hopping of electron carriers.Energyband structures experimentally extracted with the adoption of ultraviolet photoelectron spectroscopy strongly support the claimsreported in this paper.An machine learning(ML)–assisted,datadriven model of the fabricated memristor was also developedemploying different popular algorithms such as polynomialregression,support vector regression,k nearest neighbors,andartificial neural network(ANN)to model the data of the fabricateddevice.We have proposed two types of ANN models(type I andtype II)algorithms,illustrated with a detailed flowchart,to modelthe fabricated memristor.Benchmarking with standard ML techniques shows that the type II ANN algorithm provides the bestmean absolute percentage error of 0.0175 with a 98%R^(2)score.The proposed data-driven model was further validated with the characterization results of similar new memristors fabricated adoptingthe same fabrication recipe,which gave satisfactory predictions.Lastly,the ANN type II model was applied to design and implementsimple AND&OR logic functionalities adopting the fabricatedmemristors with expected,near-ideal characteristics.

关键词： Nanofiber-based memristors Data-driven modeling Artificial neural network(ANN) SnO_(2)

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Probabilistic seismic design of pile-supported building structure incorporating inherent variability of subsoil and ground motion uncertainty

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Earthquake Engineering and Engineering Vibration 2023年第1期22卷 135-156页

作者： Swagata debroy Rajib Saha Civil Engineering Department National Institute of Technology AgartalaJirania-799046Tripura(West)India

Seismic failure of structures supported on pile foundation has revealed the importance of seismic soil-foundation-structure interaction(SSFSI)for ensuring safe design.The uncertainties in subsoil properties and seismic loading may lead the problem to be more redundant.In this context,the present study attempts to assess the seismic reliability of pile foundation-supported building structure embedded in inhomogeneous clay layer considering inertial interaction.Shear strength of clay and earthquake loading is considered as spatially variable uncertain parameters.A non-linear soil-pile-structure system was assumed,and Monte Carlo simulation(MCS)was adopted to obtain probabilistic response of the system.First-order reliability method(FORM)is used for reliability assessment.The study indicates significant influence of uncertain parameters on the seismic response of building structure.Further,the influence of material and load uncertainty parameters on the probabilistic seismic response of structure designed following older version of code is higher than counterpart structure designed following recent version.FORM based reliability analysis infers thatserviceability criterion may be the governing parameter for pile foundation design.Moreover,the study also indicates that the curvature ductility demand of pile may be considered another crucial design parameter to assess the reliability of pile foundation.

关键词： seismic soil-foundation-structure interaction pile foundation soft clay Monte Carlo simulation FORM OPENSees

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Superior printed parts using history and augmented machine learning

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npj Computational Materials 2022年第1期8卷 1736-1745页

作者： Meng Jiang Tuhin Mukherjee Yang Du Tarasankar debroy State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin150001China Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPA16802USA

Machine learning algorithms are a natural fit for printing fully dense superior metallic parts since 3D printing embodies digital technology like no other manufacturing process.Since traditional machine learning needs a large volume of reliable historical data to optimize many printing variables,the algorithm is augmented with human intelligence derived from the rich knowledge base of metallurgy and physics-based models.The augmentation improves the computational efficiency and makes the problem tractable by enabling the algorithm to use a small set of data.We provide a verifiable quantitative index for achieving fully dense superior parts,facilitate material selection,uncover the hierarchy of important variables that affect the density,and present easy-to-use visual process maps.These findings can improve the quality consistency of 3D printed parts that now limit their greater industrial adaptation.The approach used here can be applied to solve other problems of 3D printing and beyond.

关键词： printing augmented consistency

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Conditions for void formation in friction stir welding from machine learning

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npj Computational Materials 2019年第1期5卷 554-561页

作者： Yang Du Tuhin Mukherjee Tarasankar debroy Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA Tianjin Key Laboratory of Advanced Joining Technology School of Materials Science and EngineeringTianjin UniversityTianjin 300350China

Friction stir welded joints often contain voids that are detrimental to their mechanical properties.Here we investigate the conditions for void formation using a decision tree and a Bayesian neural network.Three types of input data sets including unprocessed welding parameters and computed variables using an analytical and a numerical model of friction stir welding were examined.One hundred and eight sets of independent experimental data on void formation for the friction stir welding of three aluminum alloys,AA2024,AA2219,and AA6061,were analyzed.The neural network-based analysis with welding parameters,specimen and tool geometries,and material properties as input predicted void formation with 83.3% accuracy.When the potential causative variables,i.e.,temperature,strain rate,torque,and maximum shear stress on the tool pin were computed from an approximate analytical model of friction stir welding,90 and 93.3%accuracies of prediction were obtained using the decision tree and the neural network,respectively.When the same causative variables were computed from a rigorous numerical model,both the neural network and the decision tree predicted void formation with 96.6% accuracy.Among these four causative variables,the temperature and maximum shear stress showed the maximum influence on void formation.

关键词： welding friction welded

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Artificial intelligence for COVID-19 and future pandemics:A mini-review

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Journal of Acute Disease 2021年第4期10卷 135-141页

作者： Abhrajit debroy Nancy George Department of Biotechnology University Institute of BiotechnologyChandigarh UniversityMohaliPunjab 140413India

The pandemic of severe acute respiratory syndrome coronavirus 2 has spread very quickly all over the world and has become an unparalleled public health crisis.This unforeseen and exceptional situation has instigated a wave of research to investigate the virus,track its spread,and study the disease it causes.Current methods of diagnosis and monitoring largely rely on polymerase chain reactions and enzyme-linked immunesorbent assay methods.In this hour of crisis,researchers are looking for new technologies to monitor and control such disease outbreaks.Artificial intelligence(AI)is one such technology.Being an evidence-based tool,this technology has the potential to upgrade our disease management strategies and help us to restrict the spread of such diseases.AI can play an effective role in tracking the spread of diseases,screening of the population,identifying patients and developing treatments of diseases.Through this review,we aim to analyze the role of AI in the diagnosis,monitoring and treatment of diseases like coronavirus disease 2019,with most recent updates and assess the prospects of this technology in the management of such diseases.

关键词： Artificial intelligence COVID-19 Disease management Pandemic

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A Critical Assessment of Heat Transfer Models in Welding

A Critical Assessment of Heat Transfer Models in Welding

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The 2nd International Symposium on Computer-Aided Welding Engineering

作者： T. debroy Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity Park

Modern heat transfer models of welding processes can now be relied upon to understand and control weld zone geometry, temperature fields, thermal cycles and solidification parameters with a fair degree of confidence for the joining of a wide variety of materials. However, analytical models with known deficiencies continue to be widely used due to their simplicity and availability. Here a critical assessment of the progress made in the last several decades in fusion welding and more recently in friction stir welding is presented. Recent models and animations developed at Penn State will be discussed to critically examine how the modeling results can be useful to the practice of welding for improving quality and reliability of fusion welds and for improving lives of friction stir welding tools.

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从炉渣中烟化氧化亚锡

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中国有色冶金 1980年第3期 12-17页

作者： T·debroy 许孙曲

从炉渣中烟化硫化亚钖(SnS)是由炉渣分离钖的一种广泛采用的方法。尽管这个方法可从炉渣中获得很高的钖回收率(90—95%),但有SO产出。为避免环境污染,此SO必须加以处理。因此,用SnO烟化法从炉渣中回收钖具有一定的重要意义。Decroly等... 详细信息

从炉渣中烟化硫化亚钖(SnS)是由炉渣分离钖的一种广泛采用的方法。尽管这个方法可从炉渣中获得很高的钖回收率(90—95%),但有SO产出。为避免环境污染,此SO必须加以处理。因此,用SnO烟化法从炉渣中回收钖具有一定的重要意义。Decroly等人在金(尸专)铁存在时,在负压条

关键词：烟化法升/分标准温度氧压炉渣成分 SnO

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