Hostname: page-component-848d4c4894-2pzkn Total loading time: 0 Render date: 2024-06-12T23:42:59.483Z Has data issue: false hasContentIssue false
  • English
  • Français

Automatic golden device selection and measurement smoothing algorithms for microwave transistor small-signal noise modeling

Published online by Cambridge University Press:  16 June 2022

Andrei S. Salnikov Open the ORCID record for Andrei S. Salnikov [Opens in a new window] ,
Igor M. Dobush ,
Artem A. Popov Open the ORCID record for Artem A. Popov [Opens in a new window] ,
Dmitry V. Bilevich ,
Aleksandr E. Goryainov ,
Alexey A. Kalentyev  and
Aleksandr A. Metel
Andrei S. Salnikov*
Affiliation:
50ohm Lab, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina avenue, 634050 Tomsk, Russia
Igor M. Dobush
Affiliation:
50ohm Lab, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina avenue, 634050 Tomsk, Russia
Artem A. Popov
Affiliation:
50ohm Lab, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina avenue, 634050 Tomsk, Russia
Dmitry V. Bilevich
Affiliation:
50ohm Lab, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina avenue, 634050 Tomsk, Russia
Aleksandr E. Goryainov
Affiliation:
50ohm Lab, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina avenue, 634050 Tomsk, Russia
Alexey A. Kalentyev
Affiliation:
50ohm Lab, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina avenue, 634050 Tomsk, Russia
Aleksandr A. Metel
Affiliation:
50ohm Lab, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina avenue, 634050 Tomsk, Russia
*
Author for correspondence: Andrei S. Salnikov, E-mail: andrei.salnikov@main.tusur.ru
Get access Rights & Permissions [Opens in a new window]

Abstract

We propose the techniques for automatic processing of measurement results in the context of golden (typical) device selection and noise figure measurement. These techniques are for golden (typical) device selection and noise figure measurement processing. Automation of measurement result processing and microwave element modeling speeds up a modeling routine and decreases the risk of possible errors. The techniques are validated through modeling of 0.15 μm GaAs pHEMTs with 4 × 40 μm and 4 × 75 μm total gate widths. Two test amplifiers were designed using the developed models. The amplifier modeling results agree well with measurements which confirms the validity of the proposed techniques. The proposed algorithm is potentially applicable to other circuit types (switches, digital, power amplifiers, mixers, oscillators, etc.) but may require different settings in those cases. However, in the presented work, we validated the algorithm for the linear and low-noise amplifiers only.

Keywords

Automationde-embeddingGaAsgolden devicemeasurement smoothingmicrowave measurementsmicrowave transistorpHEMTsemiconductor devices and IC-technologiessmall-signal noise model
Type
Microwave Measurements
Information
International Journal of Microwave and Wireless Technologies , Volume 15 , Issue 3 , April 2023 , pp. 465 - 476
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press in association with the European Microwave Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Crupi, G and Schreurs, D (eds) (2013) Microwave de-Embedding: From Theory to Applications. Oxford: Academic Press.Google Scholar
Yattoun, and Peden, A (2006) An improved active load-pull setup for transistors large signal characterization in the Ka band. 2006 European Microwave Conference, pp. 248–251. Available at https://doi.org/10.1109/EUMC.2006.281284.CrossRefGoogle Scholar
Boglione, L, Roussos, J, Caddemi, A, Cardillo, E and Crupi, G. (2020) Device noise parameters characterization: towards extraction automation. 2020 94th ARFTG Microwave Measurement Symposium, pp. 1–4. Available at https://doi.org/10.1109/ARFTG47584.2020.9071780.CrossRefGoogle Scholar
Saraza-Canflanca, P, Diaz-Fortuny, J, Castro-Lopez, R, Roca, E, Martin-Martinez, J, Rodriguez, R, Nafria, M and Fernandez, FV (2019) New method for the automated massive characterization of bias temperature instability in CMOS transistors. 2019 Design, Automation and Test in Europe Conference and Exhibition, pp. 150–155. Available at https://doi.org/10.23919/DATE.2019.8715029.CrossRefGoogle Scholar
O'Neill, PM (2008) Production multivariate outlier detection using principal components. 2008 IEEE International Test Conference, pp. 1–10. Available at https://doi.org/10.1109/TEST.2008.4700549.CrossRefGoogle Scholar
Nakamura, Y and Tanaka, M (2010) A multi-dimensional IDDQ testing method using Mahalanobis distance. 2010 IEEE 25th International Symposium on Defect and Fault Tolerance in VLSI Systems, pp. 303–309. Available at https://doi.org/10.1109/DFT.2010.44.CrossRefGoogle Scholar
Chen, C-T, Yen, C-H, Wen, C-Y, Yang, C-H, Wu, K-C, Chern, M, Chen, Y-Y, Kuo, C-Y, Lee, J-N, Kao, S-Y and Chao, MC-T (2020) CNN-based stochastic regression for IDDQ outlier identification. 2020 IEEE 38th VLSI Test Symposium, pp. 1–6. Available at https://doi.org/10.1109/VTS48691.2020.9107570.CrossRefGoogle Scholar
Saha, SK (2018) Compact Models for Integrated Circuit Design. Boca Raton: CRC Press. Available at https://doi.org/10.1201/b19117.CrossRefGoogle Scholar
Sischka, F (2010) IC-CAP Modeling Handbook. Santa Rosa: Agilent Technologies.Google Scholar
SPAYN: golden device search algorithm, EKV MOSFET model and improved GUI, the simulation standard (1999), Silvaco. Available at https://silvaco.com/simulation-standard/spayn-golden-device-search-algorithm-ekv-mosfet-model-and-improved-gui/.Google Scholar
Aoki, H and Kobayashi, H (2016) A typical MOSFET modeling procedure for RF analog circuit design. Key Engineering Materials 698, 87–99.CrossRefGoogle Scholar
He, C, Victory, J, Xiao, Y, De Vleeschouwer, H, Zheng, E and Hu, Z (2020) SiC mosfet corner and statistical SPICE model generation. In 2020 32nd International Symposium on Power Semiconductor Devices and ICs (ISPSD). IEEE, pp. 154–157. Available at https://doi.org/10.1109/ISPSD46842.2020.9170091.Google Scholar
Tsukamoto, H, Shintani, M and Sato, T (2020) Statistical extraction of normally and lognormally distributed model parameters for power MOSFETs. IEEE Transactions on Semiconductor Manufacturing 33, 150–158.CrossRefGoogle Scholar
Yoshitomi, S (2020) Combination of transistors’ compact model and big data for successful smart factory. In 2020 International Symposium on Devices, Circuits and Systems (ISDCS). IEEE, pp. 1–6. Available at https://doi.org/10.1109/ISDCS49393.2020.9262981.Google Scholar
Chen, Z, Xu, Y, Zhang, B, Chen, T, Gao, T and Xu, R (2016) A GaN HEMTs nonlinear large-signal statistical model and its application in S-band power amplifier design. IEEE Microwave and Wireless Components Letters 26, 128–130.CrossRefGoogle Scholar
Vincent, B, Hathwar, R, Kamon, M, Ervin, J, Schram, T, Chiarella, T, Demuynck, S, Baudot, S, Siew, Y K, Kubicek, S, Litta, E D, Chew, S and Mitard, J (2020) Process variation analysis of device performance using virtual fabrication: methodology demonstrated on a CMOS 14-nm FinFET vehicle. IEEE Transactions on Electron Devices 67, 5374–5380.CrossRefGoogle Scholar
Deng, M, Quémerais, T, Bouvot, S, Gloria, D, Chevalier, P, Lépilliet, S, Danneville, F and Dambrine, G (2017) Small-signal characterization and modelling of 55nm SiGe BiCMOS HBT up to 325GHz. Solid-State Electronics 129, 150–156.CrossRefGoogle Scholar
Popov, A, Bilevich, D, Salnikov, A, Dobush, I, Goryainov, A, Kalentyev, A and Metel, A (2020) A combined technique for amplifier oriented small-signal noise model extraction. International Journal of RF and Microwave Computer-Aided Engineering 30, 1–12. https://doi.org/10.1002/mmce.22273.CrossRefGoogle Scholar
Chen, D, Wu, Y, Liu, H, Yin, WY and Kang, K (2018) A scalable model of on-chip inductor including tunable dummy metal density factor. IEEE Transactions on Components, Packaging and Manufacturing Technology 9, 296–305.CrossRefGoogle Scholar
Arnal, A, Teres, L and Ramon, E (2020) An organic process design kit, from characterization to modelling and simulation. 2020 IEEE 8th Electronics System Integration Technology Conference, pp. 1–5. Available at https://doi.org/10.1109/ESTC48849.2020.9229883.CrossRefGoogle Scholar
Fattori, M, Fijn, IA, Hu, L, Cantatore, E, Torricelli, F and Charbonneau, M (2019) Circuit design and design automation for printed electronics. 2019 Design, Automation and Test in Europe Conference and Exhibition IEEE, pp. 42–47. Available at https://doi.org/10.23919/DATE.2019.8715095.CrossRefGoogle Scholar
Srimani, T, Hills, G, Bishop, M, Lau, C, Kanhaiya, P, Ho, R, Amer, A, Chao, M, Yu, A, Wright, A, Ratkovich, A, Aguilar, D, Bramer, A, Cecman, C, Chov, A, Clark, G, Michaelson, G, Johnson, M, Kelley, K, Manos, P, Mi, K, Suriono, U, Vuntangboon, S, Xue, H, Humes, J, Soares, S, Jones, B, Burack, S, Arvind, , Chandrakasan, A, Ferguson, B, Nelson, M and Shulaker, MM (2020) Heterogeneous integration of BEOL logic and memory in a commercial foundry: multi-tier complementary carbon nanotube logic and resistive RAM at a 130nm node. 2020 IEEE Symposium on VLSI Technology, pp. 1–2. Available at https://doi.org/10.1109/VLSITechnology18217.2020.9265083.CrossRefGoogle Scholar
Tukey, JW (1977) Exploratory Data Analysis. Reading: Addison-Wesely.Google Scholar
Cho, SY (2018) Non-linear FIR smoothing filter for systems with a modelling error and its application to the DR/GPS integrated navigation. IET Radar, Sonar & Navigation 12, 882–888.CrossRefGoogle Scholar
Kwon, BK, Han, S, Kwon, OK and Kwon, WH (2007) Minimum variance FIR smoothers for discrete-time state space models. IEEE Signal Processing Letters 14, 557–560.CrossRefGoogle Scholar
Yin, L, Yang, R, Gabbouj, M and Neuvo, Y (1996) Weighted median filters: a tutorial. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 43, 157–192.Google Scholar
Savitzky, A and Golay, MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36, 1627–1639.CrossRefGoogle Scholar
Keysight Technologies. High-Accuracy Noise Figure Measurements Using the PNA-X Series Network Analyzer. Santa Rosa, CA, USA: Keysight Technol., Appl. Note 5990-5800.Google Scholar
Tayrani, R, Gerber, JE, Daniel, T, Pengelly, RS and Rohde, UL (1993) A new and reliable direct parasitic extraction method for MESFETs and HEMTs. 23rd European Microwave Conference, pp. 451–453. Available at https://doi.org/10.1109/EUMA.1993.336593.CrossRefGoogle Scholar
Lu, S-S, Chen, T-W, Chen, H-C and Meng, C (2001) The origin of the kink phenomenon of transistor scattering parameter S/sub 22/. IEEE Transactions on Microwave Theory and Techniques 49, 333–340.CrossRefGoogle Scholar
Crupi, G, Raffo, A, Caddemi, A and Vannini, G (2015) Kink effect in S22 for GaN and GaAs HEMTs. IEEE Microwave and Wireless Components Letters 25, 301–303.CrossRefGoogle Scholar