[paper] Parameter Extraction Approaches for Memristor Models

Dmitry Alexeevich Zhevnenko^1,2, Fedor Pavlovich Meshchaninov^1,2, Vladislav Sergeevich Kozhevnikov^1,2, Evgeniy Sergeevich Shamin^1,2, Oleg Alexandrovich Telminov^1,2, and Evgeniy Sergeevich Gornev^1,2

Research and Development of Parameter Extraction Approaches for Memristor Models

Micromachines 2021, 12, 1220. 

DOI: 10.3390/mi12101220

   
1 Moscow Institute of Physics and Technology, Moscow, Russia;
2 JSС MERI, Zelenograd, Russia

Abstract: Memristors are among the most promising devices for building neural processors and non-volatile memory. One circuit design stage involves modeling, which includes the option of memristor models. The most common approach is the use of compact models, the accuracy of which is often determined by the accuracy of their parameter extraction from experiment results. In this paper, a review of existing extraction methods was performed and new parameter extraction algorithms for an adaptive compact model were proposed. The effectiveness of the developed methods was confirmed for the volt-ampere characteristic of a memristor with a vertical structure: TiN/HfxAl1-xOy/HfO2/TiN.

Fig: Model VACs with different numbers of inhomogeneities: 

(a) four inhomogeneities; (b) no inhomogeneities.

Acknowledgments: This research was funded by the Ministry of Science and Higher Education of the Russian  Federation, grant number 075-15-2020-791. Authors thank the Institute of Microelectronics Technology and High-Purity Materials RAS for access to experimental data on the study of graphene oxide memristor switching cycles.

[paper] Novel Design and Optimization and the gm/ID Ratio

A Novel Design and Optimization Approach for Low Noise Amplifiers (LNA) Based on MOST Scattering Parameters and the gm/ID Ratio

Juan L. Castagnola¹*, Fortunato C. Dualibe² Agustín M. Laprovitta¹ and Hugo García-Vázquez³

¹Facultad de Ingeniería, Universidad Católica de Córdoba, Córdoba 5017 (AN)

²Service d’Électronique et Microélectronique, Université de Mons (UMONS), 7000 Mons (BE)

³Departamento de Electrónica, Instituto de Astrofísica de Canarias (IAC), 38200 La Laguna (SP)

* Author to whom correspondence should be addressed.

Electronics 2020, 9(5), 785; https://doi.org/10.3390/electronics9050785

Received: 31 March 2020 / Revised: 30 April 2020 

Accepted: 9 May 2020 / Published: 11 May 2020

Abstract - This work presents a new design methodology for radio frequency (RF) integrated circuits based on a unified analysis of the scattering parameters of the circuit and the gm/ID ratio of the involved transistors. Since the scattering parameters of the circuits are parameterized by means of the physical characteristics of transistors, designers can optimize transistor size and biasing to comply with the circuit specifications given in terms of S-parameters. A complete design of a cascode low noise amplifier (LNA) in 65nm CMOS technology is taken as a case study in order to validate the approach. In addition, this methodology permits the identification of the best trade-off between the minimum noise figure and the maximum gain for the LNA in a very simple way.

Figure: gm/ID versus iD

Acknowledgement - This research was funded by Universidad Católica de Córdoba (Argentina), the Walloon Region DGO6 BEWARE Fellowships Academia Programme (1410164-POHAR, cofunded by the European Marie Curie Actions), the Belgian FNRS (Fond National pour la Recherche Scientifique) and the Argentinean MINCyT (Ministerio de Ciencia y Tecnología).

levmar : Levenberg-Marquardt nonlinear least squares algorithms in C/C++

Introduction page: <http://users.ics.forth.gr/~lourakis/levmar/> 

The site provides GPL native ANSI C implementations of the Levenberg-Marquardt optimization algorithm, usable also from C++, Matlab, Perl, Python, Haskell and Tcl and explains their use. Both unconstrained and constrained (under linear equations, inequality and box constraints) Levenberg-Marquardt variants are included. The Levenberg-Marquardt (LM) algorithm is an iterative technique that finds a local minimum of a function that is expressed as the sum of squares of nonlinear functions. It has become a standard technique for nonlinear least-squares problems and can be thought of as a combination of steepest descent and the Gauss-Newton method. When the current solution is far from the correct one, the algorithm behaves like a steepest descent method: slow, but guaranteed to converge. When the current solution is close to the correct solution, it becomes a Gauss-Newton method.

Interfaces for using levmar from high-level programming environments & languages such as Matlab, Perl Python, Haskell and Tcl are also available; please refer to the FAQ for more details.