#SilTerra - #MEMS Technology Roadmap https://t.co/X0g0M0ImoL #paper https://t.co/n0Nthns32a

#SilTerra - #MEMS Technology Roadmap https://t.co/X0g0M0ImoL #paper pic.twitter.com/n0Nthns32a

— Wladek Grabinski (@wladek60) June 19, 2020

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[Short Course] Modeling and Simulation of Nano-Transistors

Short Course

Modeling and Simulation of Nano-Transistors

6 - 10 July 2020 at Outreach Auditorium,IIT Kanpur

http://www.iitk.ac.in/nanolab/sc2020/

by Prof. Yogesh S. Chauhan

Nanolab, IIT Kanpur

http://home.iitk.ac.in/~chauhan/

Aim: VLSI design will soon use transistors whose size will be as small as 10nm. The aim of this short course is to educate and train bright minds on different aspects of Nano-transistors. Modeling especially compact modeling is the heart of circuit simulation. TCAD simulations are used for early device design and to understand the internal physics of transistor. Electrical characterization includes current and capacitance voltage measurement of transistor. RF measurement is an exciting area which involves understanding of devices as well as high frequency effects. This short course will cover various topics in modeling, simulation and characterization of transistors especially at nanoscale.

Topics: (1) VLSI design and Nanoelectronics, (2) Physics and Operation of MOSFET, (3) SPICE and Circuit simulation, (4) TCAD simulation: Theory and demonstration, (5) Compact Modeling: Theory and demonstration, (6) Scaling and Moore's Law, (7) Nano-Transistors: FinFET, FDSOI, Negative Capacitance FET, Nanosheet FETs, 2D-FETs etc. (8) Characterization: Current and capacitance measurement, (9) RF CMOS and GaN High Electron Mobility Transistors

Hands-on Sessions: (1) Verilog-A coding, (2) SPICE ckt. Simulation, (3) TCAD Simulation, (4) Parameter Extraction

Coordinator: Prof. Yogesh S. Chauhan Dept. of Electrical Engg., IIT Kanpur

Registration: This short course has been postponed to end of this year or early next year due to ongoing pandemic. New Dates will be announced once normalcy returns in the country.

A Benchmark Study Of Complementary-Field Effect Transistor (#FET) Process Integration Options: Comparing #Bulk vs. #SOI vs. DSOI Starting Substrates https://t.co/rYE24rym7L #paper https://t.co/T3ECdVJa5c

A Benchmark Study Of Complementary-Field Effect Transistor (#FET) Process Integration Options: Comparing #Bulk vs. #SOI vs. DSOI Starting Substrates https://t.co/rYE24rym7L #paper pic.twitter.com/T3ECdVJa5c

— Wladek Grabinski (@wladek60) June 17, 2020

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June 17, 2020 at 05:02PM
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[paper] CV of Graphene–Silicon Heterojunction Photodiodes

Sarah Riazimehr,  Melkamu Belete,  Satender Kataria,  Olof Engström and Max Christian Lemme

Capacitance–Voltage (C –V) Characterization 

of Graphene–Silicon Heterojunction Photodiodes

Advanced Optical Materials 

First Published Open Access: 07 May 2020

DOI: 10.1002/adom.202000169

Abstract: Heterostructures of 2D and 3D materials form efficient devices for utilizing the properties of both classes of materials. Graphene/silicon (G/Si) Schottky diodes have been studied extensively with respect to their optoelectronic properties. Here, a method to analyze measured capacitance–voltage (C –V) data of G/Si Schottky diodes connected in parallel with G/silicon dioxide/Si (GIS) capacitors is introduced. The accurate extraction of the built‐in potential (Φbi) and the Schottky barrier height (SBH) from the measurement data independent of the Richardson constant is also demonstrated.

Figure 2

Fig.: a) Cross section of the test device showing both MIS and GIS regions. b) Small‐signal C –V characteristics of Dtest (line) compared to a theoretically calculated C –V curve (dashed ) at 10 kHz.

Acknowledgements: Financial support from the European Commission (Graphene Flagship, 785219, 881603) and the German Ministry of Education and Research, BMBF (GIMMIK, 03XP0210) is gratefully acknowledged.

[paper] Compact Model for Ferroelectric FET

Lu, Darsen, Sourav De, Mohammad Aftab Baig, Bo-Han Qiu, and Yao-Jen Lee

Computationally efficient compact model for ferroelectric field-effect transistors 

to simulate the online training of neural networks

Semiconductor Science and Technology (2020)

DOI: 10.1088/1361-6641/ab9bed

Abstract: In this paper, a compact drain current formulation that is simple and adequately computationally efficient for the simulation of neural network online training was developed for the ferroelectric memory transistor. Tri-gate ferroelectric field effect transistors (FETs) with Hf0.5Zr0.5O2 gate insulators were fabricated with a gate-first high-k metal gate CMOS process. Ferroelectric switching was confirmed with double sweep and pulse programming and erasure measurements. Novel characterization scheme for drain current was proposed with minimal alteration of ferroelectric state in subthreshold for accurate threshold voltage measurements. The resultant threshold voltage exhibited highly linear and symmetric across multilevel states. The proposed compact formulation accurately captured the FET gate-bias dependence by considering the effects of series resistance, Coulomb scattering, and vertical field dependent mobility degradation.

Fig.: Transmission electron micrograph of the fabricated tri-gate Fe

finFET device across the fin, with approximately 60 nm fin width, 30 nm fin

height, and 10 nm HZO Fe layer.

Acknowledgements: This work was jointly supported by the Ministry of Science and Technology (Taiwan) grant MOST–108–2634–F–006–08 and is part of research work by MOST’s AI Biomedical Research Center. We are grateful to the Taiwan Semiconductor Research Institute for nanofabrication facilities and services and to Dr. Wen-Jay Lee and Nan-Yow Chen of the National Center for High-Performance Computing for helpful suggestions on AI computation. This manuscript was edited by Wallace Academic Editing.

#Samsung #MOSIS Collaboration https://t.co/IOrXK5W1Y8 #paper https://t.co/VXZqf03bmY

#Samsung #MOSIS Collaboration https://t.co/IOrXK5W1Y8 #paper pic.twitter.com/VXZqf03bmY

— Wladek Grabinski (@wladek60) June 17, 2020

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Learning with brain chemistry https://t.co/UJRbFdHuUh #paper https://t.co/PB4Ty0moUg

Learning with brain chemistry https://t.co/UJRbFdHuUh #paper pic.twitter.com/PB4Ty0moUg

— Wladek Grabinski (@wladek60) June 16, 2020

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June 16, 2020 at 05:39PM
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