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May 13, 2020

#OpenRAMAN: #OpenSource Raman Spectrometer Is Cheaper, But Not Cheap https://t.co/SAKV61rdou https://t.co/SotKelrOVK

#OpenRAMAN: #OpenSource Raman Spectrometer Is Cheaper, But Not Cheap https://t.co/SAKV61rdou pic.twitter.com/SotKelrOVK
— Wladek Grabinski (@wladek60) May 13, 2020

from Twitter https://twitter.com/wladek60

May 13, 2020 at 10:22AM
via IFTTT

May 12, 2020

[paper] Computing-in-Memory

Computing-in-Memory for Performance and Energy Efficient Homomorphic Encryption

Dayane Reis, Student Member, IEEE, Jonathan Takeshita, Taeho Jung, Member, IEEE, Michael Niemier, Senior Member, IEEE and Xiaobo Sharon Hu, Fellow, IEEE

preprint arXiv:2005.03002 (2020).

Abstract - Homomorphic encryption (HE) allows direct computations on encrypted data. Despite numerous research efforts, the practicality of HE schemes remains to be demonstrated. In this regard, the enormous size of ciphertexts involved in HE computations degrades computational efficiency. Near-memory Processing (NMP) and Computing-in-memory (CiM) — paradigms where computation is done within the memory boundaries — represent architectural solutions for reducing latency and energy associated with data transfers in data-intensive applications such as HE. This paper introduces CiM-HE, a Computing-in-memory (CiM) architecture that can support operations for the B/FV scheme, a somewhat homomorphic encryption scheme for general computation. CiM-HE hardware consists of customized peripherals such as sense amplifiers, adders, bit-shifters, and sequencing circuits. The peripherals are based on CMOS technology, and could support computations with memory cells of different technologies. Circuit-level simulations are used to evaluate our CiM-HE framework assuming a 6T-SRAM memory. We compare our CiM-HE implementation against (i) two optimized CPU HE implementations, and (ii) an FPGA-based HE accelerator implementation.When compared to a CPU solution, CiM-HE obtains speedups between 4.6x and 9.1x, and energy savings between 266.4x and 532.8x for homomorphic multiplications (the most expensive HE operation). Also, a set of four end-toend tasks, i.e., mean, variance, linear regression, and inference are up to 1.1x, 7.7x, 7.1x, and 7.5x faster (and 301.1x, 404.6x, 532.3x, and 532.8x more energy efficient). Compared to CPUbased HE in a previous work, CiM-HE obtain 14.3x speed-up and >2600x energy savings. Finally,our design offers 2.2x speed-up with 88.1x energy savings compared to a state-of-the-art FPGAbased accelerator.

Fig: Log shifter implemented in CiM-HE.

This work was supported in part by ASCENT, one of six centers in JUMP, a Semiconductor Research Corporation (SRC) program sponsored by DARPA.

Corresponding author: Xiaobo Sharon Hu, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA. e-mail: .

May 11, 2020

Conference Paper Reached 500 Reads

Wladek

Wladek Grabinski, Daniel Tomaszewski, Farzan Jazaeri, Anurag Mangla, Jean-Michel Sallese, Maria-Anna Chalkiadaki, Antonios Bazigos, and Matthias Bucher

FOSS EKV 2.6 Parameter Extractor

22nd International MIXDES Conference, pp. 181-186 (2015)

Abstract: The design of advanced integrated circuits (IC) in particular for low power analog and radio-frequency (RF) application becomes more complex as the device level modeling confronting challenges in micro- and nano-meter CMOS processes. As present CMOS technologies continue geometry scaling the designers can benefit using dedicated SPICE MOSFET models and apply specific analog design methodologies. The EKV was developed especially to meet altogether the analog/RF design requirements. This paper describes a basic set of the DC parameter extraction steps for the EKV 2.6 model. The free open source software (FOSS) Profile2D tool was used to illustrate an accurate EKV 2.6 DC extraction strategy.

[paper] BSIM-HV: High-Voltage MOSFET Model

H. Agarwal , Member, IEEE, C. Gupta , Graduate Student Member, IEEE, R. Goel , Graduate Student Member, IEEE, P. Kushwaha , Member, IEEE, Y.-K. Lin , Graduate Student Member, IEEE, M.-Y. Kao , Graduate Student Member, IEEE, J.-P. Duarte , Graduate Student Member, IEEE, H.-L. Chang , Member, IEEE, Y. S. Chauhan , Senior Member, IEEE, S. Salahuddin, Fellow, IEEE, and C. Hu, Life Fellow, IEEE

BSIM-HV: High-Voltage MOSFET Model Including Quasi-Saturation and Self-Heating Effect

IEEE TED, vol. 66, no. 10, pp. 4258-4263, Oct. 2019

doi: 10.1109/TED.2019.2933611

Abstract - A BSIM-based compact model for a high-voltage MOSFET is presented. The model uses the BSIM-BULK (formerly BSIM6) model at its core, which has been extended to include the overlap capacitance due to the drift region as well as quasi-saturation effect. The model is symmetric and continuous, is validated with the TCAD simulations and experimental 35- and 90V LDMOS and 40V VDMOS transistors, and shows excellent agreement.

FIG: Schematic of the LDMOS. Lightly doped n-region constitutes the drain. Majority of the applied drain voltage drops across this region, which protects the intrinsic transistor region from breakdown.

Manuscript received March 3, 2019; revised May 23, 2019 and July 24, 2019; accepted July 31, 2019. Date of publication August 26, 2019; date of current version September 20, 2019. This work was supported in part by the members of the Berkeley Center for Negative Capacitance Technology and the members of the Berkeley Device Modeling Center. The review of this article was arranged by Editor B. Iñiguez.

[paper] Compact Device Models for FinFET and Beyond

D. D. Lu, M. V. Dunga, A. M. Niknejad, C.Bing Hu, F.-X. Liang, W.-C. Hung, J. Lee, C.-H. Hsu
and M.-H. Chiang,

Compact device models for FinFET and beyond

ArXiv, vol. abs/2005.02580, 2020

Abstract - Compact device models play a significant role in connecting device technology and circuit design. BSIM-CMG and BSIM-IMG are industry standard compact models suited for the FinFET and UTBB technologies, respectively. Its surface potential based modeling framework and symmetry preserving properties make them suitable for both analog/RF and digital design. In the era of artificial intelligence / deep learning, compact models further enhanced our ability to explore RRAM and other NVM-based neuromorphic circuits. We have demonstrated simulation of RRAM neuromorphic circuits with Verilog-A based compact model at NCKU. Further abstraction with macromodels is performed to enable larger scale machine learning simulation.

Fig: Simulation of a novel floating - gate synaptic transistor. (a) Device structure with separate negative feedback gate (nfb) for programming and synaptic gate (sg) readout. (b) Equivalent circuit diagram for compact modeling

Acknowledgements - The authors would like to express sincere gratitude to Chip Implementation Center (CIC), Hsinchu, Taiwan for providing SPICE simulation environment for RRAM simulations.

May 7, 2020

[PhD] Compact DC Modeling of Tunnel-FETs

Compact DC Modeling of Tunnel-FETs

November 2019

PhD Thesis of Fabian Horst

Doctor Advisor: Profs. Benjamin Iniguez and Alexander Kloes

Abstract - In the last decade, the tunnel field-effect transistor (TFET) has gained a lot of interest and is handled as a possible successor of the conventional MOSFET technology. The current transport of a TFET is based on the band-to-band (B2B) tunneling mechanism and therefore, the subthreshold slope at room temperature can overcome the limit of 60 mV/dec. In order to describe and analyze the TFET behavior in circuit simulations, this dissertation introduces a compact DC model for double-gate TFETs. The modeling approach considers the B2B tunneling and the parasitic effect of trap-assisted tunneling (TAT) in the ON- and AMBIPOLAR-state of the TFET. It includes a 2D compact potential equation package to de-scribe the band diagram of the TFET. Based on the band diagram, the B2B tunneling and TAT current part are derived separately. In order to do so, firstly a compact expression for the tunneling length is found, which is then used together with a numerical robust Wentzel-Kramers-Brillouin (WKB) approach to calculate the tunneling probability. Afterwards, using Landauer’s tunneling equation, the tunneling generation rate is calculated and approximated to come to a closed-form expression for the current density. Further approximation of the current density by a mathematical function, compact expressions for the resulting B2B tun-neling and TAT current are achieved. The verification of the model is done with the help of TCAD Sentaurus simulation data for various simulation setups. Furthermore, the validity of the model is proven by measurements of fabricated complementary TFETs. In order to demonstrate the numerical stability and continuity as well as the flexibility, simulations of TFET-based logic circuits like a single-stage inverter or an SRAM cell are performed and analyzed. The combination of the DC model with an TFET AC model allows for a transient simulation of an 11-stage ring oscillator.

Fig: 2D sketch of the n-type DG TFET device geometry, showing the channel thickness t ch , the channel length l ch , the gate oxide thickness tox and the length of the S/D region l sd . Source (S) and drain (D) region are highly p/n-doped with a doping concentration N s/d

URL: http://hdl.handle.net/10803/668957

May 6, 2020

IEEE EDS DL Series by the EDS Delhi Chapter

	IEEE Electron Device Society (EDS) Delhi Chapter – India & Department of Electronic Science University of Delhi South Campus, New Delhi, India
Jointly Organizes

EDS Distinguished Lecture

(Live Session under EDS Distinguished Lecturer Program - Virtual Lectures)

Online Live Webinar Lecture Schedule (via Google Meet)

April 30, 2020 at 10:30 am (past event) High-k Dielectric and Interface Engineering for High Performance Si/Ge MOS and FinFETs	Kuei-Shu Chang-Liao Department of Engineering and System Science National Tsing Hua University, Hsinchu, Taiwan
May 01, 2020 at 10:30 am (past event) Two-dimensional Layered Materials for Nanoelectronics	Yang Chai Associate Professor, Department of Applied Physics The Hong Kong Polytechnic University
May 05, 2020 at 01:30 pm (past event) Introducing two-dimensional layered dielectrics in solid-state micro-electronic devices	Mario Lanza Institute of Functional Nano & Soft Materials, Soochow University, Collaborative Innovation Center of Suzhou Nano Science & Technology, China
May 06, 2020 at 06:30 pm (past event) Field Effect Transistors: From MOSFET to Tunnel-FET	Joao Antonio Martino Professor at University of Sao Paulo, Brazil
May 08,2020 at 06:30 pm IST Junctionless Nanowire Transistors: Electrical Characteristics and Compact Modeling	Marcelo Antonio Pavanello Centro Universitario FEI, Department of Electrical Engineering Av. Humberto de Alencar Castelo Branco, Sao Bernardo do Campo, Brazil
May 11, 2020 at 01:30 pm IST From CMOS to Neuromorphic Computing - A peek into the future	Maria Merlyne De Souza Department of Electronic and Electrical Engineering The University of Sheffield, United Kingdom
May 12, 2020 at 10:30 am IST Phase change electro-optical devices for space applications	Mina Rais-Zadeh Group Supervisor, Advanced Optical and Electromechanical Microsystems Group, Micro Device Laboratory, NASA JPL, Pasadena, CA
May 15, 2020 at 08:30 pm IST State-of-the-Art Silicon Very Large Scale Integrated Circuits: Industrial Face of Nanotechnology	Michael S. Shur Electrical, Computer and Systems Engineering and Physics, Applied Physics, and Astronomy Rensselaer Polytechnic Institute
May 16, 2020 at 02:00 pm IST Transparent and Flexible Large Area Electronics	Arokia Nathan Cambridge Touch Technologies, University of Cambridge, United Kingdom (UK)
May 20, 2020 at 02:30 pm IST Trends and challenges in Nanoelectronics for the next decade	Elena Gnani Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy
May 22, 2020 at 07:30 pm IST Accelerating commercialization of SiC power electronics	Victor Veliadis Executive Director and CTO, Power America Professor of Electrical and Computer Engineering, North Carolina State University
May 27, 2020 at 07:30 pm IST Advanced III-N Devices for 5G and Beyond	Patrick Fay Department of Electrical Engineering, University of Notre Dame