[chapter] Low-Voltage Analog IC Design

Deepika Gupta¹

Low-Voltage Analog Integrated Circuit Design

Nanoscale VLSI. Book series (ESIEE) (2020) pp 3-22

DOI: 10.1007/978-981-15-7937-0_1

¹Department of Electronics and Communication Engineering, IIIT Naya Raipur, India

Abstract: In this chapter, we review the challenges and effective design techniques for ultra-low-power analog integrated circuits. With the miniaturization, having low-power low-voltage mixed signal IC is essential to maintain the electric field in the device. This constraint presents bottleneck for the researchers to design robust analog circuits. Specifically, the low value of supply voltage with small technology influences many specifications of analog IC, e.g., power supply rejection, dynamic range and immunity to noise, etc. In addition, it also affects the ability of the MOS transistor to be operated in the strong inversion region. Note that with the technology reduction, power supply VDD is reducing but the threshold voltage VT is not decreasing proportionally to maintain low leakage current. However, this process reduces the overdrive voltage and limits the staking of transistors. In this case, the transistor can be made to work in weak inversion to work and reduce the power consumption. Further, reduction in VDD to achieve low-power consumption causes many other circuit-related issues such as PVT variations, degradation of dynamic range, mismatching in circuits element and differential paths. There have been many design methods developed for the ultra-low-power analog ICs. In this chapter, we will discuss some of the design techniques to reduce the power consumption in analog ICs. In addition, we will also discuss the basic building blocks of analog circuits with discussed design techniques. The charge-based EKV model can be a very suitable example of a MOS simulation model to be used in all inversion regions of transistor operations [Enz 2017]. In EKV model, the smallest number of core parameters is needed for the accurate behavioral modeling of transistor. Particularly, charge-based EKV model is beneficial for the analysis of analog circuits because it allows the analysis with simple calculations over different inversion regions. Hence, developing new device simulation models specific for analog circuit design is crucial.

Fig: Vth and Vdd scaling trend vs. Leff  [Zhao 2006]

References:

[Enz 2018] Enz C, Chicco F, Pezzotta A (2017) Nanoscale MOSFET modeling-part 1: the simplified EKV model for the design of low-power analog circuits. IEEE Solid-State Circuits Magazine 9(3):26–35

[Zhao 2006] Zhao W, Cao Y (2006) New generation of predictive technology model for sub-45 nm early design exploration. IEEE Trans Electron Devices 53(11):2816–2823

[paper] TCAD-Machine Learning Framework

Hiu Yung Wong¹ (Senior Member, IEEE), Ming Xiao², Boyan Wang², Yan Ka Chiu¹, Xiaodong Yan³, Jiahui Ma³, Kohei Sasaki⁴, Han Wang³ (Senior Member, IEEE)

and Yuhao Zhang² (Member, IEEE)

TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis with Experimental Demonstration

IEEE J-EDS, vol. 8, pp. 992-1000, 2020

doi: 10.1109/JEDS.2020.3024669.

¹Department of Electrical Engineering, San Jose State University, San Jose, CA 95112, USA
²Virginia Polytechnic Institute, State University, Blacksburg, VA 24060, USA
³Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA
⁴Development Department, Novel Crystal Technology Inc., Sayama 3501328, Japan

Abstract: This work, for the first time, experimentally demonstrates a TCAD-Machine Learning (TCADML) framework to assist the analysis of device-to-device variation and operating (ambient) temperature without the need of physical quantities extraction. The ML algorithm used in this work is the Principal Component Analysis (PCA) followed by third order polynomial regression. After calibrated to limited ‘expensive’ experimental data, ‘low cost’ TCAD simulation is used to generate a large amount of device data to train the ML model. The ML was then used to identify the root cause of device variation and operating temperature from any given experimental current-voltage (I-V) characteristics. We applied this framework to study the ultra-wide-bandgap gallium oxide (Ga2O3) Schottky barrier diode (SBD), an emerging device technology that holds great promise for temperature sensing, RF, and power applications in harsh environments. After calibration, over 150,000 electrothermal TCAD simulations are performed with random variation of physical parameters (anode effective work function, drift layer doping, and drift layer thickness) and operating temperature. An ML model is trained using these TCAD data and we found 1,000-10,000 TCAD data can train an accurate machine. We show that without physical quantities extraction, performing PCA is essential for the TCAD trained ML model to be applicable to analyze experimental characteristics. The physical parameters and temperatures predicted by the ML model show good agreement with experimental analysis. Our TCAD-ML framework shows great promise to accelerate the development of new device technologies with a significantly more efficient process of material and device experimentation.

FIG: Flow chart diagram of the proposed TCAD-Machine Learning framework. All components are demonstrated in this article except the MLDatabase which stores previously trained ML algorithms.

Acknowledgment: The authors thank Dr. Pooya Jannaty of Cruise and Dr. Philip Leong of the University of Sydney for the discussion of ML algorithms. The experimental work is in part supported by the Southeastern Center for Electrical Engineering Education program and the High Density Integration industry mini-consortium of the Center for Power Electronics Systems at Virginia Tech.

[paper] Metamaterial for Wearable Applications

Kabir Hossain^1,2, Thennarasan Sabapathy^1,2, Muzammil Jusoh^1,2, Ping Jack Soh1^1,2 Ainur Fasihah Mohd Fazilah^1,2, Ahmad Ashraf Abdul Halim^1,2, N. S. Raghava³, Symon K. Podilchak⁴, Dominique Schreurs⁵, Qammer H. Abbasi⁶

ENG and NZRI Characteristics of Decagonal Shaped Metamaterial for Wearable Applications

International Conference on UK-China Emerging Technologies 

UCET, Glasgow, United Kingdom, 2020, pp. 1-4, 

doi: 10.1109/UCET51115.2020.9205409

¹Advanced Communication Engineering (ACE) Centre of Excellence, Universiti Malaysia Perlis, No 15 & 17, Jalan Tiga, Pengkalan Jaya Business Centre, 01000 Kangar, Perlis, Malaysia. 

²School of Computer and Communication Engineering, Universiti Malaysia Perlis, Kampus Alam UniMAP Pauh Putra, Arau 02600, Malaysia 

³Departments of Electronics and Communication Engineering, Delhi Technological University, India
⁴Institute of Digital Communications, School of Engineering, University of Edinburgh, EH9 3FB, UK
⁵ESAT-TELEMIC Research Division, KU Leuven, Kasteelpark Arenberg 10 Box 2444, 3001 Leuven, Belgium 

⁶James Watt School of Engineering, University of Glasgow, UK

Abstract: A decagonal-shaped split ring resonator metamaterial based on a wearable or textile-based material is presented in this work. Analysis and comparison of various structure sizes are compared considering a compact 6×6 mm2 metamaterial unit cell, in particular, where robust transmissionreflection (RTR) and Nicolson-Ross-Weir (NRW) methods have been performed to extract the effective metamaterial parameters. An investigation based on the RTR method indicated an average bandwidth of 1.39 GHz with a near-zero refractive index (NZRI) and a 2.35 GHz bandwidth when considering epsilon negative (ENG) characteristics. On the other hand, for the NRW method, approximately 0.95 GHz of NZRI bandwidth and 2.46 GHz of ENG bandwidth have been observed, respectively. These results are also within the ultrawideband (UWB) frequency range, suggesting that the proposed unit cell structure is suitable for textile UWB antennas, biomedical sensors, related wearable systems, and other wireless body area network communication systems.

Fig: The real NZRI values obtained using the RTR and NRW methods for different unit cell structures: (a) 1 1 × array, (b) 2 1× array, (c) 1 2 × array, and (d) 2 2 × array

Acknowledgment: The author would like to acknowledge the support from the Fundamental Research Grant Scheme (FRGS) under a grant number of FRGS/1/2019/TK04/UNIMAP/02/3 from the Ministry of Education Malaysia.

[paper] X-Parameters Based Characterization and Compact Modeling of SiGe HBT Linearity

[paper] G. Niu, Anni Zhang, Yiao Li, Huaiyuan Zhang, Andries Scholten, Marnix Willemsen, Ralf Pijper and L.F. Tiemeijer; X-Parameters Based Characterization and Compact Modeling of SiGe HBT Linearity; ECS Transactions 2020, Volume 98, Number 5 https://t.co/PzJnIjkFC8 #semi pic.twitter.com/7IAIkqfPjF

— Wladek Grabinski (@wladek60) October 8, 2020

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October 08, 2020 at 05:22PM
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Special IJHSES Issue on Advancements in Smart Grid Technologies

International Journal of High Speed Electronics and Systems

ISSN (print): 0129-1564 | ISSN (online): 1793-6438

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Call for Papers

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Special Issue on Advancements in Smart Grid Technologies

This special issue is on electrical power generation, transmission, distribution and utilization in smart grid, from the viewpoints of individual power system elements and their integration, interaction and technological advancement.

The special issue focuses on microelectronic systems, circuits, power control and soft computing techniques in smart grid. It includes, but are not limited to, the following:

• Renewable & Sustainable Energy Technologies
• Cloud-assisted smart grid architectures and development
• Internet-centric smart grid solutions
• Case studies on recent advances in smart grid and renewable energy system
• Information and communication technology for enhancing smart grid and renewable energy system
• Future of renewable energy sources in environmental protection
• Sustainable computational methods to evaluate the optimization of renewable energy systems
• Networking and data mining in smart grids for continuous sustainable development
• Threat, challenges & opportunity of integrating smart grid and renewable energy system
• Generation techniques ranging from advances in conventional electromechanical methods, through nuclear power generation, to renewable energy generation.
• A study on the smart grid and renewable energy system for reducing the complexity of power grids
• Distribution techniques, equipment development, and smart grids.
• Renewable power generation and clean energy technologies
• Distributed energy resources and storage
• Modern power grid devices, sensors and wireless technologies

Paper Submission and Review Schedule:

• First announcement: October. 12th 2020
• Submission Deadline: November30th 2020
• Final notification: January 10, 2020
• Publication Date: June 30th 2020

Camera ready articles should be sent to the Guest Editor for consideration. Please specify the research topic on the cover page.

IJHSES Editor-in-Chiefs:
Michael Shur, Rensselaer Polytechnic Institute (USA)
Wladek Grabinski, MOS-AK (EU)

Guest Editor:
Naresh Kumar Yadav, D.C.R.U.S.T, Murthal (India)