[paper] MEMS Sensors Reliability

M. Hommel^a, H. Knab^a, S. Galal Yousef^b

Reliability of automotive and consumer MEMS sensors - An overview

Microelectronics Reliability (114252) online Oct. 11, 2021

DOI: 10.1016/j.microrel.2021.114252

a Robert-Bosch-GmbH, Automotive Electronics, Tübinger Str. 123, 72762 Reutlingen, Germany
b Bosch Sensortec GmbH, Gerhard-Kindler-Str. 9, 72770 Reutlingen, Germany

Abstract: In our daily life, sensors play more and a more important role. They take over many functions in the automotive world as well as in consumer products with an increasing dissemination of the internet of things. In addition, they offer a broad variety of new applications. Sensors are typically build up in a package including a sensing element (e.g. micromechanical structures in acceleration sensors or membranes in gas sensors, etc.) and a microelectronic chip to evaluate the sensor data. This article will give an overview, how the reliability of such a system is validated. The challenges for reliability in terms of requirements and qualification for automotive and consumer applications will be discussed. The complex structure of a sensor module in combination with a broad variety of materials implies many possible failure mechanisms, which have to be considered. Some relevant sensor failure mechanisms caused by mechanical shock, thermo-mechanical stress and the influence of humidity on sensor reliability will be shown. The challenges for describing the influence of humidity on the sensor lifetime by an acceleration model will be discussed in detail. Finally, the paper will give an outlook for the reliability challenges of future sensor applications.

Fig: Loads on a MEMS sensor module.

IEEE-EDS Santa Clara Valley/San Francisco Chapter October Seminar (Webex only)

Title: TCAD/SPICE-Augmented Machine Learning for Defect and Variation Study

Speaker: Dr. Hiu Yung Wong, San Jose State University

Friday, October 15, 2021 at noon – 1PM PDT

Register Here

Webex link will be distributed to the registrant via email.
Organizer contact: John Choi (wonhochoi at micron.com)

Abstract:

In semiconductor technology development, it is desirable to pinpoint the source of defect or variation through electrical measurements, which are non-destructive and have much higher throughput than the traditional failure analysis. This can be achieved through machine learning which is a powerful tool for correlating the electrical characteristics to the nature of the defect/variation. However, a good machine is only possible with enough well-controlled training data, which is difficult to obtain experimentally. TCAD and SPICE simulations which are well-calibrated to experimental data are proposed to generate the training data.

In this talk, we will first demonstrate the use of TCAD to generate data to train machines to deduce the epitaxial layer thickness of Si p-i-n diodes and the workfunction and operating temperature variation of Ga2O3 Schottky Barrier Diodes, based solely on the measured electrical characteristics. We will emphasize the use of minimal domain expertise to obviate the difficulties in feature extraction. We will also demonstrate the techniques that are important to make the TCAD-trained machine applicable to predicting experimental data. SPICE-augmented ML will be demonstrated for detecting contact resistance degradation in inverters. Finally, we will discuss the use of TCAD-augmented machines to help reverse engineering and understand novel devices.

Speaker Bio:

Hiu Yung Wong is an Assistant Professor in the EE department, San Jose State University. He received his Ph.D. degree in Electrical Engineering and Computer Science from the University of California, Berkeley in 2006. From 2006 to 2009, he worked as a Technology Integration Engineer in Spansion. From 2009 to 2018, he was a TCAD Senior Staff Application Engineer in Synopsys, during which he received the Synopsys Excellence Award in 2010. In 2021, he received the NSF CAREER award and the Newnan Brothers Award for Faculty Excellence.

His research interests include the applications of machine learning in simulation and manufacturing, cryogenic electronics, quantum computing, reliability simulations, wide bandgap devices (such as GaN, SiC, Ga2O3, and diamond) simulations, novel semiconductor devices design, and Design Technology Co-Optimization (DTCO). His work has produced 80 papers and 10 issued patents.

Call for Officer(s):

The Santa Clara Valley Chapter of the EDS is seeking candidates to apply for positions on the organizing executive committee for 2022. In particular we are looking for folks interested in becoming webmaster/communications director and secretary, although we welcome applications for treasurer, vice-chair, and chair as well. If you are interested in helping us organize technical talks and otherwise delivering value to EDS members in your local community, please email vijay_narasimhan@ieee.org, EDS SCV Chapter Chair, to express your interest.

More information at the IEEE EDS Santa Clara Valley-San Francisco Chapter Home Page

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[paper] WEAF Mnecosystem

J. Iannacci^1,2

The WEAF Mnecosystem: a perspective of MEMS/NEMS technologies

as pillars of future 6G, tactile internet and super-IoT

Microsystem Technologies, Oct. 5, 2021.

DOI:10.1007/s00542-021-05230-3

1 Center for Sensors and Devices (DS), FBK Trento, Italy
2 InnerBlender, Bologna, Italy

Abstract: The future 6G and tactile internet (TI) paradigms pose challenges and demand for requirements that are far beyond what the 5G—today at its dawn—will ever achieve. The classical approaches in designing devices, systems and infrastructures will not be suitable to build the AI-driven (Artificial Intelligence) 6G network. This work envisages a critical part for MEMS/NEMS technologies in making 6G turn into reality. Such a leading role sits on a reformulation of the common concept of Hardware (HW) triggered by Micro/Nanotechnologies and Materials. To this end, the WEAF Mnecosystem, i.e. the Water, Earth, Air and Fire Micro/Nanotechnologies Ecosystem, is conceived, leveraging the analogy with the four classical elements in nature, and is explained in details in the following pages, along with the discussion of some reference examples. In a nutshell, Earth and Air are the classical concepts of the Hardware (HW) and Software (SW), respectively. Water is the novel formulation of the concept of HW, which, like water, is liquid in terms of functional characteristics and gains, at the same time, some features typical of the SW (i.e. Air). Fire, eventually, is the HW devoted to harvest, store and transfer energy, raising its level of abstraction to the concept of heat, which flows from warmer to cooler bodies.

Fig: Schematic of the self-recovery design solution [i]: 

a.) Complete schematic of the RF-MEMS micro-relay; 

b.) schematic with the MEMS membrane made invisible 

in order to show the underlying micro-heater

Acknowledgements: The author wishes herewith to express his gratitude to Ms. Moira Osti for designing and realizing all the images included in this work. The author also wants to sincerely say thanks to Brando and Pietro, for accompanying the writing of parts of this work with their energy and unconditional serenity.

REF:

[i] Iannacci J, Repchankova A, Faes A, Tazzoli A, Meneghesso G, Niessner M (2010) Experimental investigation on the exploitation of an active mechanism to restore the operability of malfunctioning RF-MEMS switches. Proc Eng 5:734–737. DOI: /10.1016/j.proeng.2010.09.213

#Samsung #Foundry: #2nm Silicon in 2025

#Samsung #Foundry: #2nm Silicon in 2025

 

#semi #chip #manufacturing https://t.co/i57huVn0Kd

— Wladek Grabinski (@wladek60) Oct 7, 2021

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from Twitter https://twitter.com/wladek60

October 07, 2021 at 03:57PM
via IFTTT

[paper] Compact Schottky-barrier CNTFET Modeling

Manojkumar Annamalai and Michael Schroter

Compact formulation for the bias dependent quasi-static mobile charge in Schottky-barrier CNTFETs IEEE Transactions on Nanotechnology (2021)

DOI: 10.1109/TNANO.2021.3116694

CEDIC, Technische Universität Dresden (D)

Abstract: Carbon nanotube (CNT) field-effect transistors (FETs) are promising candidates for future high-frequency (HF) system-on-chip applications. Understanding and modeling mobile charge storage on CNTs is therefore essential for device optimization and circuit design. A physics-based compact analytical formulation is presented that enables an accurate approximation of the mobile charge in Schottky-barrier CNTFETs over the practically relevant bias range for HF circuit design. The formulation is C∞ continuous and yields accurate results also for the capacitances. The new formulation has been verified for both ballistic and scattering dominated carrier transport by employing device simulation, which was calibrated to experimental data from multi-tube CNTFETs.

Fig: Band diagram in a CNTFET along the axial direction (left red arrow) and, with applied gate bias, along the radial direction perpendicular to the gate (right blue arrow).

Acknowledgments: The authors would like to thank Dr. S. Mothes, formerly with CEDIC, for valuable discussions regarding the device simulator. This project was financially supported in part by the German National Science Foundation (DFG SCHR695/6-2).