[Habilitation] Assessment of novel devices in CMOS technology

Assessment of novel devices in CMOS technology

by electrical characterization and physics-based model

Habilitation Presented To Obtain The Authorization 

To Direct Research From Sorbonne University

Lionel Trojman, PhD

Sorbonne Université, 2020

Organization of the thesis

Chapter 1: This chapter extends research work after the author’s PhD study. It focuses on HfO2-based dielectric MOSFETs with sub-1nm EOT. The study explores the impact of transport factors like saturation velocity on planar MOSFETs and the mobility of FDSOI-UTBB MOSFETs. Notably, the back-biased effect is considered, and an inversion charge model is developed for different front and back biases.

Chapter 2: Emphasis the application of the statistical defect-centric model to assess the impact of channel hot carriers on the reliability of low-dimensional MOSFETs.

Chapter 3: This chapter shifts focus to GaN-on-Si wafer devices for power electronic applications. These devices integrate MOS-like structures into III-V material-based devices, specifically MOS-HEMT and GET-SBD.

Chapter 4: Investigates RERAM devices. It stems from cooperative research with UNICAL and a PhD program in collaboration with Aix-Marseille University

FIG: Description of the gate structure (half device) of the studied device including the parasitic capacitance inner fringing (CIF), outer fringe (COF) and Junction overlap capacitance (COV)

 

 

IEEE 5NANO2024 Conference 25-26th April, 2024

IEEE 5NANO2024

2024 IEEE International Conference on Nanoelectronics, Nanophotonics,
Nanomaterials, Nanobioscience & Nanotechnology

25th & 26th April 2024

VISAT Engineering College

Elanji, Ernakulam, Kerala, India - 686 665.

The IEEE 5NANO2024 International Conference is going to be dynamic and informative as it provides the premier interdisciplinary forum for researchers, practitioners and educators to present and discuss the most recent innovations, trends, practical challenges encountered, and the solutions adopted in the field of Nanotechnology. The theme of the conference is: “Future Challenges and Advanced Innovations in Nanotechnology”

Contact 5NANO2024:

Dr. T.D.Subash, Conference Organizing Chair - 5NANO2024,
VISAT Engineering College,
Elanji, Ernakulam, Kerala, India - 686 665

Tel: +91 9447691397, +91 9486881397.
E-mail: deanresearch@visat.ac.in, tdsubash2007@gmail.com, 5nano2k24@gmail.com

Website: https://www.5nano2024.com

[paper] in-memory computing using FeFET

Taha Soliman, Swetaki Chatterjee, Nellie Laleni, Franz Müller, Tobias Kirchner, Norbert Wehn, Thomas Kämpfe, Yogesh Singh Chauhan and Hussam Amrouch

First demonstration of in-memory computing crossbar using multi-level Cell FeFET

Nat Commun 14, 6348 (2023)

DOI: 10.1038/s41467-023-42110-y

1 Robert Bosch GmbH, Renningen, Germany
2 Semiconducture Test and Reliability, University of Stuttgart, Stuttgart, Germany
3 Department of Electrical Engineering, IIK, Kanpur, India
4 Fraunhofer IPMS, Dresden, Germany
5 RPTU Kaiserslautern-Landau, Kaiserslautern, Germany
6 MIRMI; Technical University of Munich, Germany

Abstract: Advancements in AI led to the emergence of in-memory-computing architectures as a promising solution for the associated computing and memory challenges. This study introduces a novel in-memory-computing (IMC) crossbar macro utilizing a multi-level ferroelectric field-effect transistor (FeFET) cell for multi-bit multiply and accumulate (MAC) operations. The proposed 1FeFET-1R cell design stores multi-bit information while minimizing device variability effects on accuracy. Experimental validation was performed using 28 nm HKMG technology-based FeFET devices. Unlike traditional resistive memory-based analog computing, our approach leverages the electrical characteristics of stored data within the memory cell to derive MAC operation results encoded in activation time and accumulated current. Remarkably, our design achieves 96.6% accuracy for handwriting recognition and 91.5% accuracy for image classification without extra training. Furthermore, it demonstrates exceptional performance, achieving 885.4 TOPS/W–nearly double that of existing designs. This study represents the first successful implementation of an in-memory macro using a multi-state FeFET cell for complete MAC operations, preserving crossbar density without additional structural overhead.

FIG: a.) The material stack of FeFETs. 

b.) The multi-bit FeFET can be programmed to different states

to store the weight of the synapse

Acknowledgements: This work has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826655 and No 876925. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Belgium, France, Germany, Portugal, Spain, The Netherlands, Switzerland. Open Access funding enabled and organized by Projekt DEAL.

[paper] Symmetric BSIM-SOI

Chetan Kumar Dabhi, Dinesh Rajasekharan, Girish Pahwa, Debashish Nandi, Naveen Karumuri, Sreenidhi Turuvekere, Anupam Dutta, Balaji Swaminathan, Srikanth Srihari, Yogesh S. Chauhan, Sayeef Salahuddin, and Chenming Hu

Symmetric BSIM-SOI: A Compact Model for Dynamically Depleted SOI MOSFETs 

 in IEEE TED (2024)

Part I DOI: 10.1109/TED.2024.3363110

Part II DOI: 10.1109/TED.2024.3363117

1 Department of Electrical Engineering and Computer Sciences, UCB, CA, USA

2 Department of Electrical Engineering, IIT Kanpur, India

3 GlobalFoundries, Bengaluru, India

Abstract: In this article, we present a symmetric surface-potential-based model for dynamic depletion (DD) device operation of silicon-on-insulator (SOI) FETs for RF and analog IC design applications. The model accurately captures the device behavior in partial depletion (PD) and full depletion (FD) modes, as well as in the transition from PD to FD, based on device geometry, doping, and bias conditions. The model also exhibits an excellent source–drain symmetry during dc and small-signal simulations, resulting in error-free higher order harmonics. The model is fully scalable with bias, temperature, and geometry and has been validated extensively with real device data from the industry. The symmetric BSIM-SOI model is developed in Verilog-A and compatible with all commercial SPICE simulators.

FIG: (a) Schematic of a typical SOI MOSFET

(b) Cgg versus Vgb for different substrate bias, with the PD-to-FD transition 

Acknowledgment: The authors thanks the members of the Compact Model Coalition (CMC), particularly Geoffrey J. Coram and Jushan Xie, for testing the model and suggesting improvements. The authors appreciate the CMC QA team’s efforts in conducting a model quality check. Caixia Han and Xiao Sun from Cadence provided a few useful test cases. They thank Ananth Sundaram and Anamika Singh Pratiyush from GlobalFoundries India for the help and discussion regarding DDSOI model intricacies and development. Model code is available at BSIM Website <https://bsim.berkeley.edu/models/bsimsoi/>

SSCS April Technical Webinar

SSCS April Technical Webinar

Getting Started with Open Source Silicon, Presented By: Matthew Venn

Abstract: In this presentation, Matt Venn will share his experience of getting started with chip design using the free and open source tools. Going from zero to 20 chips in 3 years, there are plenty of successes and failures to share. Matt will then move on to sharing the best resources, inspirational example projects, and showcase some of his own tools. The presentation will finish with a demonstration showing just how easy and cheap it is to get your own chip manufactured today.

Biography: Matt Venn is a science & technology communicator and electronic engineer. He has been involved with open source silicon for the last 3 years and has sent 20 chips for manufacture. He has helped over 600 people learn the tools, with 300 people taking part in manufacturable designs:

• https://zerotoasiccourse.com/
• https://tinytapeout.com

Date: 2024-04-19 Time: 11 AM ET

Location Webinar - Online

Contact Aeisha VanBuskirk – a.vanbuskirk@ieee.org

Register Here