Jan 14, 2021

[paper] Fabrication EM AlGaN/GaN MIS HEMT

Flavien Cozette1, Bilal Hassan1, Christophe Rodriguez1, Eric Frayssinet2, Rémi Comyn2, François Lecourt3, Nicolas Defrance4, Nathalie Labat5, François Boone1, Ali Soltani1, Abdelatif Jaouad1, Yvon Cordier2 and Hassan Maher1
New barrier layer design for the fabrication of gallium nitride-metal-insulator-semiconductor-high electron mobility transistor normally-off transistor
2021 Semicond. Sci. Technol. 36 034002
DOI: 10.1088/1361-6641/abd489

1LN2, CNRS-UMI-3463, 3IT, Université de Sherbrooke, Canada
2Université Côte d'Azur, CNRS, CRHEA, Valbonne, France
3OMMIC, 94450 Limeil-Brévannes, France
4IEMN, CNRS-UMR-8520, University of Lille, France
5IMS, CNRS-UMR-5218, University of Bordeaux, France

Abstract: This paper reports on the fabrication of an enhancement-mode AlGaN/GaN metal-insulator-semiconductor-high electron mobility transistor with a new barrier epi-layer design based on double Al0.2Ga0.8N barrier layers separated by a thin GaN layer. Normally-off transistors are achieved with good performances by using digital etching (DE) process for the gate recess. The gate insulator is deposited using two technics: plasma enhance chemical vapour deposition (sample A) and atomic layer deposition (sample B). Indeed, the two devices present a threshold voltage (Vth) of +0.4 V and +0.9 V respectively with ΔVth about 0.1 V and 0.05 V extracted from the hysteresis gate capacitance measurement, a gate leakage current below 2 × 10−10 A mm−1, an ION/IOFF about 108 and a breakdown voltage of VBR = 150 V and 200 V respectively with 1.5 µm thick buffer layer. All these results are indicating a good barrier surface quality after the gate recess. The DE mechanism is based on chemical dissolution of oxides formed during the first step of DE. Consequently, the process is relatively soft with very low induced physical damages at the barrier layer surface.
Fig: SEM image of an E-mode device.

Acknowledgments: This work was supported by Fonds de Recherches du Québec—Nature, Technologies (FRQNT), the Natural Sciences and Engineering Research Council of Canada (NSERC), French technology facility network RENATECH and the French National Research Agency (ANR) through the projects ED-GaN (ANR-16-CE24-0026-02) and the 'Investissements d'Avenir' program GaNeX (ANR-11-LABX-0014).

Imec’s Plan For Continued Scaling: “Towards Atomic Channels and Deconstructed Chips” https://t.co/rUAJ5qPJOO #semi https://t.co/CiRNgqPsHG



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Jan 13, 2021

IEEE-EDS SCV/SF Chapter January Seminar (Webex only)

Title: Compute-in-Memory with Emerging Nonvolatile-Memories: Challenges and Prospects
Speaker: Prof. Shimeng Yu, Georgia Institute of Technology
Friday, January 15, 2020 at noon – 1PM PDT
Please note that this seminar is now WEBEX participation only:

Webex Link 

Organizer contact: Hiu Yung Wong <hiuyung.wong@ieee.org>

Abstract: Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall problem in the deep learning accelerator. In this presentation, first I will present our DNN+NeuroSim benchmark framework that is interfaced with Tensorflow/PyTorch to evaluate different device technologies for state-of-the-art DNN models. We will discuss about the pros and cons of various non-volatile memory candidates and the most important device specifications for inference/training, respectively. Second, I will present our RRAM-CIM prototype chips that are integrated with CMOS peripheral circuitry and its performance. Furthermore, we will show our experimental characterizations of the multilevel RRAM's variability and reliability and their impact on DNN inference accuracy. To overcome the challenges of the RRAM-CIM prototypes we identified, we propose monolithic 3D integration with back-end-of-line (BEOL) transistors as a potential solution.

Speaker Bio: Shimeng Yu is an associate professor of electrical and computer engineering at the Georgia Institute of Technology. He received the B.S. degree in microelectronics from Peking University in 2009, and the M.S. degree and Ph.D. degree in electrical engineering from Stanford University in 2011 and 2013, respectively. From 2013 to 2018, he was an assistant professor at Arizona State University. Prof. Yu's research interests are nanoelectronic devices and circuits for energy-efficient computing systems. His expertise is on the emerging non-volatile memories (e.g., RRAM, ferroelectrics) for different applications such as deep learning accelerator, neuromorphic computing, monolithic 3D integration, and hardware security. Among Prof. Yu's honors, he was a recipient of the NSF Faculty Early CAREER Award in 2016, the IEEE Electron Devices Society (EDS) Early Career Award in 2017, the ACM Special Interests Group on Design Automation (SIGDA) Outstanding New Faculty Award in 2018, the Semiconductor Research Corporation (SRC) Young Faculty Award in 2019, and the ACM/IEEE Design Automation Conference (DAC) Under-40 Innovators Award in 2020, etc. Prof. Yu is active in professional services. He served or is serving many premier conferences as technical program committee, including IEEE International Electron Devices Meeting (IEDM), IEEE Symposium on VLSI Technology, etc. He is a senior member of the IEEE.