Prof. Mathieu Luisier appointed as Full Professor of Computational Nanoelectronics at D-​ITET

Prof. Mathieu Luisier appointed as Full Professor of Computational Nanoelectronics at D-​ITET.

Congratulations!
#ethzurich #electricalengineering #professor #semi

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Zero to ASIC Course https://t.co/65Dy5oHGPD #semi https://t.co/TnON0j7M8h

Zero to ASIC Course https://t.co/65Dy5oHGPD #semi https://t.co/TnON0j7M8h

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[paper] Cryogenic Temperature Effects in 10-nm Bulk CMOS FinFETs

Sujit K. Singh, Sumreti Gupta, Reinaldo A. Vega* and Abhisek Dixit

Accurate Modeling of Cryogenic Temperature Effects in 10-nm Bulk CMOS FinFETs Using the BSIM-CMG Model

in IEEE Electron Device Letters

DOI: 10.1109/LED.2022.3158495.

  

 Indian Institute of Technology, New Delhi (IN)

*IBM Research, Albany, NY (USA)

Abstract: In this letter, we have proposed modifications to the existing BSIM-CMG compact model to enhance its ability to model the behavior of short channel bulk FinFETs (both n and p-type) from room temperature down to cryogenic temperatures (10K). The proposed model is highly accurate in capturing the subthreshold swing, threshold voltage, and effective mobility trends observed in FinFET cryogenic operation. For efficient optimization of the proposed model parameters, we have proposed an adequate modeling strategy. We have compared convergence time between the existing BSIM-CMG model and the proposed model by simulating a reasonably large circuit using pseudo-inverters.

Fig (a) TEM image of the fin cross-section (b) Measured device layout-related parameters 

[paper] Ultra-Low-Power Imaging System

Andrea Bejarano-Carbo, Hyochan An, Kyojin Choo, Shiyu Liu, Dennis Sylvester, 

David Blaauw and Hun-Seok Kim, 

Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring

inyML Research Symposium’22, March 2022, San Jose, CA

rXiv:2203.04496v1 [eess.SP] 9 Mar 2022

  

University of Michigan, Ann Arbor, Michigan, USA

ABSTRACT Millimeter-scale embedded sensing systems have unique advantages over larger devices as they are able to capture, analyze, store, and transmit data at the source while being unobtrusive and covert. However, area-constrained systems pose several challenges, including a tight energy budget and peak power, limited data storage, costly wireless communication, and physical integration at a miniature scale. This paper proposes a novel 6.7×7×5mm imaging system with deep-learning and image processing capabilities for intelligent edge applications, and is demonstrated in a home-surveillance scenario. The system is implemented by vertically stacking custom ultra-low-power (ULP) ICs and uses techniques such as dynamic behavior-specific power management, hierarchical event detection, and a combination of data compression methods. It demonstrates a new image-correcting neural network that compensates for nonidealities caused by a mm-scale lens and ULP front-end. The system can store 74 frames or offload data wirelessly, consuming 49.6μW on average for an expected battery lifetime of 7 days.

Fig: Imager system cross-section

Acknowledgments:The authors would like to thank Sony Semiconductor Solutions Corp./Sony electronics Inc. for supporting this work.

Faster #analog #computer could be based on mathematics of complex systems

Faster #analog #computer could be based on mathematics of complex systems https://t.co/A0iTUZK2U9 #semi https://t.co/Cn0cfGZHqZ

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