Carlo Gilardi
Ph.D. Student in Electrical Engineering, admitted Autumn 2018
All Publications
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Band-to-Band Tunneling Leakage Current Characterization and Projection in Carbon Nanotube Transistors.
ACS nano
2023
Abstract
Carbon nanotube (CNT) transistors demonstrate high mobility but also experience off-state leakage due to the small effective mass and band gap. The lower limit of off-current (IMIN) was measured in electrostatically doped CNT metal-oxide-semiconductor field-effect transistors (MOSFETs) across a range of band gaps (0.37 to 1.19 eV), supply voltages (0.5 to 0.7 V), and extension doping levels (0.2 to 0.8 carriers/nm). A nonequilibrium Green's function (NEGF) model confirms the dependence of IMIN on CNT band gap, supply voltage, and extension doping level. A leakage current design space across CNT band gap, supply voltage, and extension doping is projected based on the validated NEGF model for long-channel CNT MOSFETs to identify the appropriate device design choices. The optimal extension doping and CNT band gap design choice for a target off-current density are identified by including on-current projection in the leakage current design space. An extension doping level >0.5 carrier/nm is required for optimized on-current.
View details for DOI 10.1021/acsnano.3c04346
View details for PubMedID 37910857
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Ultra-Dense 3D Physical Design Unlocks New Architectural Design Points with Large Benefits
IEEE. 2023
View details for Web of Science ID 001027444200118
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Extended Scale Length Theory for Low-Dimensional Field-Effect Transistors
IEEE TRANSACTIONS ON ELECTRON DEVICES
2022
View details for DOI 10.1109/TED.2022.3190464
View details for Web of Science ID 000829075200001
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Bandgap Extraction at 10 K to Enable Leakage Control in Carbon Nanotube MOSFETs
IEEE ELECTRON DEVICE LETTERS
2022; 43 (3): 490-493
View details for DOI 10.1109/LED.2022.3141692
View details for Web of Science ID 000761656500042
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Extended Scale Length Theory Targeting Low-Dimensional FETs for Carbon Nanotube FET Digital Logic Design-Technology Co-optimization
IEEE. 2021
View details for DOI 10.1109/IEDM19574.2021.9720672
View details for Web of Science ID 000812325400173