Show simple item record

dc.contributor.authorLi, Mingda
dc.date.accessioned2018-10-23T13:22:12Z
dc.date.available2019-06-04T06:01:45Z
dc.date.issued2018-05-30
dc.identifier.otherLi_cornellgrad_0058F_10888
dc.identifier.otherhttp://dissertations.umi.com/cornellgrad:10888
dc.identifier.otherbibid: 10489457
dc.identifier.urihttps://hdl.handle.net/1813/59372
dc.description.abstractThe performance of computing systems has been increasingly choked by power consumption and memory access time within and between system components. Meanwhile, the explosion of artificial intelligence requires massive data-heavy computation. Therefore, it is crucial to develop energy efficient computing from devices to architectures. This work is developed along three streams: a steep device with low operation voltage, a novel device enabling complex logic operation, and an efficient modeling algorithm to quickly incorporate emerging devices into circuit designs. On the first front, tunnel field effect transistors (TFETs), which switch by modulating quantum tunneling, promise sub-60 mV/dec subthreshold swing and operate at low power consumption. Based on the unique properties of atomically thin 2D layered materials, two-dimensional heterojunction interlayer tunneling field effect transistor (Thin-TFET) was proposed as a ultra-scaled steep transistor. On the second front, we converted the “undesirable” ambipolar behavior in TFETs into XNOR logic operation, and proposed a one-transistor XNOR design: TransiXNOR. On the third front, we structured artificial neural networks with awareness of device physics, and developed an accurate, efficient, and generic device compact modeling algorithm: physics-inspired neural network (Pi-NN).
dc.language.isoen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDevice Modeling
dc.subjectTFET
dc.subjectTunneling
dc.subjectElectrical engineering
dc.subjectComputer science
dc.subjectneural network
dc.subject2D material
dc.subjectTransistor
dc.titleTUNNEL FIELD EFFECT TRANSISTORS: FROM THEORY TO APPLICATIONS
dc.typedissertation or thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Electrical and Computer Engineering
dc.contributor.chairXing, Huili Grace
dc.contributor.committeeMemberCardie, Claire T.
dc.contributor.committeeMemberJena, Debdeep
dcterms.licensehttps://hdl.handle.net/1813/59810
dc.identifier.doihttps://doi.org/10.7298/X47M065C


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International

Statistics