[paper] Compact Modeling in MFIS Negative-Capacitance FETs

N. Pandey and Y. S. Chauhan

Analytical Modeling of Short-Channel Effects in MFIS Negative-Capacitance FET

Including Quantum Confinement Effects

in IEEE TED (Early Access), DOI: 10.1109/TED.2020.3022002.

Abstract: An analytical 2-D model of double-gate metal-ferroelectric-insulator-semiconductor-negative-capacitance FET (MFIS-NCFET), using Green's function approach, in the subthreshold region, is presented in this article. The explicit solution of coupled 2-D Landau-Devonshire and Poisson equations is analytically derived. Subsequently, an analytical and explicit model of subthreshold slope is developed from potential functions. The developed model includes quantum-mechanical effects, which considers not only geometrical confinements but also electrical confinements. The analytical solution of a 2-D nonhomogeneous Poisson equation coupled with the 1-D Schrödinger equation is used to obtain the potential function in the channel. The impact of the ferroelectric thickness (tfe) on quantum confinement is also studied. We find that larger tfe reduces the quantum confinement effect. Therefore, as tfe increases, threshold voltage roll-off with the variation in Si-body thickness decreases.

Fig: Schematic of DG MFIS-NCFET.

Aknowegement: This work was supported in part by the Swarna Jayanti Fellowship under Grant DST/SJF/ETA-02/2017-18 and in part by the FIST Scheme of the Department of Science and Tech- nology under Grant SR/FST/ETII-072/2016. 

[paper] gm/ID-Based Sizing for Analog ICs

Tuotian Liao and Lihong Zhang

An LDE-Aware gm/ID-Based Hybrid Sizing Method for Analog Integrated Circuits

Analog Integrated Circuits. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1–1. doi:10.1109/tcad.2020.3025068 

Abstract: Layout-dependent effects (LDEs) have become increasingly more important in the synthesis of analog integrated circuits. In this paper, a two-phase hybrid sizing method for high performance analog circuits is proposed. It consists of gm/ID-based device characterization, circuit modeling, sensitivity-based constraints for LDEs, and mixed-integer nonlinear programming in the first phase, and many-objective evolutionary algorithm (many OEA) based sizing in the second phase. In the first phase, accurate device characterization is handled with little modeling effort thanks to the gm/ID design methodology. Then the LDE parameters that are linked to the normalized DC current are further optimized with the aid of sensitivity analysis. Thus, a variety of electrical, geometrical, and LDE-related constraints can be conveniently integrated into modeling of the sizing problem. In the second phase, the many OEA-based sizing refiner can further optimize the LDE parameters by using more detailed layout information via our proposed model. A new floor plan variation scheme is also applied to improve computation efficiency and enhance optimization effectiveness. The experimental results demonstrate high efficacy of our proposed methodology in LDE-aware analog sizing optimization.

Fig: Module-level of the LDE-aware gm/ID EA two-phase synthesis flow

Thanks to the contribution of the EKV model [1], inversion coefficient (IC) can be used to indicate the biasing inversion level of a MOSFET. This helped Binkley et al. [2] change the design freedom from the conventional W, L, and ID to IC, L, and ID. Since IC is related to DC bias, device geometry, and device characteristics (e.g., gm/ID), it can reflect performance tradeoff (e.g., intrinsic gain vs. bandwidth) of a single MOSFET. In [3], bias information rather than gm/ID parameters was set as variables, while a small-scale LUT was built to find MOSFET aspect ratio (i.e., W/L) and eventually W.

Aknowlegement: This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), Research and Development Corporation (RDC) of Newfoundland and Labrador, and Memorial University of Newfoundland.

References:

1. C. Enz, F. Chicco, and A. Pezzotta, “Nanoscale MOSFET modeling: Part 1: The simplified EKV model for the design of low-power analog circuits,” IEEE Solid-State Circuits Mag., vol. 9, no. 3, pp. 26–35, 2017.
2. D. M. Binkley, C. E. Hopper, S.D. Tucker, B.C. Moss, J. M. Rochelle, and, D. P. Foty, “A CAD methodology for optimizing transistor current and sizing in analog CMOS design,” IEEE Trans. Comput-Aided Design Integr. Circuits Syst., vol. 22 no. 2, pp. 225-237, 2003.
3. C.-W. Lin, P.-D. Sue, Y.-T. Shyu, and S.-J. Chang, “A bias-driven approach for automated design of operational amplifiers,” in Proc. Int.

[paper] oTFT Charge-Based Variability Model

Aristeidis Nikolaou, Ghader Darbandy, Jakob Leise, Jakob Pruefer, James W. Borchert, Michael Geiger, Hagen Klauk, Benjamin Iñiguez, Fellow, IEEE,

and Alexander Kloes, Senior Member, IEEE

Charge-Based Model for the Drain-Current Variability in Organic Thin-Film Transistors 

Due to Carrier-Number and CorrelatedMobility Fluctuation

in IEEE TED (early access), DOI: 10.1109/TED.2020.3018694.

Abstract: In this study, a consistent analytical chargebased model for the bias-dependent variability of the drain current of organic thin-film transistors is presented. The proposed model combines both charge-carrier-numberfluctuation effects and correlated-mobility-fluctuation effects to predict the drain-current variation and is verified using experimental data acquired from a statistical population of organic transistors with various channel dimensions, fabricated on flexible polymeric substrates in the coplanar or the staggered device architecture.

Fig: a) Cross section of the organic TFTs fabricated in the inverted coplanar (bottom-gate, bottom-contact) architecture. b) Transistor channel divided into a noisy element between positions x and x + δx and two noiseless transistors of channel lengths x and L − x, respectively. c) Small-signal representation.

Acknowledgment: This work was supported in part by the German Federal Ministry of Education and Research “SOMOFLEX” under Grant 13FH015IX6 and in part by the German Research Foundation (DFG) under Grant KL 1042/9-2 (SPP FFlexCom). The authors would like to thank AdMOS GmbH for support.

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[paper] Ion-Gated Transistors

Ion-Gated Transistor: An Enabler for Sensing and Computing Integration

Xianbao Bu, Han Xu, Dashan Shang, Yue Li, Hangbing Lv, and Qi Liu

Advanced Intelligent Systems, p.2000156.

DOI: 10.1002/aisy.202000156

Abstract: With the rapid development of the Internet of Things, the amount of data we involved in our daily life is growing exponentially, which poses significant challenges for data processing and transmission to the conventional terminal sensors that passively acquire external data. Inspired by biological sensory nervous systems, building artificial intelligent sensory systems with both sensing and computing capability is regarded as a promising way to address these challenges, by which the acquired data can be preprocessed locally and timely before transmitting them to the remote server for further processing. Ion-gated transistors (IGTs), which have been widely used in sensors and have been recently investigated for neuromorphic computing, exhibit great potential in this domain. Herein, the essential operation principles, device structures, and electrical characteristics of IGT are introduced, and the recent developments in biosensors, neuromorphic computing, and intelligent sensors with near-sensor computing and in-sensor computing modes are summarized. To conclude, the current challenges and future development of IGT for intelligent sensory systems are presented.

Fig: (a) Optical micrograph displaying the top view of an individual IGT (top right) and IGT array conforming to the surface of a human hand (bottom left). (b) Sample traces of in vivo signals acquired by IGTs, reflecting the wide span of frequency and amplitude characteristics.  

Acknowledgements: X.B. and H.X. contributed equally to this work. This work was supported by the National Key R&D Program of China under grant no. 2018YFA0701500; the National Natural Science Foundation of China under grant nos. 61874138, 61821091, 61825404, 61732020, and 61851402; the Strategic Priority Research Program of the Chinese Academy of Sciences under grant no. XDB44000000; Major Scientific Research Project of Zhejiang Lab (grant no. 2019KC0AD02); and Beijing Academy of Artificial Intelligence (BAAI).