Affiliation:
1. Polytech’Lab, Université Côte d’Azur, UPR UniCA 7498, 06903 Sophia Antipolis, France
Abstract
Although Moore’s Law reaches its limits, it has never applied to analog and RF circuits. For example, due to the short channel effect (SCE), drain-induced barrier lowering (DIBL), and sub-threshold slope (SS)…, longer transistors are required to implement analog cells. From 22 nm CMOS technology and beyond, for reasons of variability, the channel of the transistors has no longer been doped. Two technologies then emerged: FinFET transistors for digital applications and UTBB FDSOI transistors, suitable for analog and mixed applications. In a previous paper, a new topology was proposed utilizing some advantages of the FDSOI technology. Thanks to this technology, a novel cross-coupled back-gate (BG) technique was implemented to improve analog and mixed signal cells in order to reduce the surface of the integrated circuit. This technique was applied to a current mirror to reduce the small channel effect and to provide high-output impedance. It was demonstrated that it is possible to overcompensate the SCE and DIBL effects and to create a negative output resistor. This paper presents a new LC tank oscillator based on this current mirror functioning as a negative resistor.
Reference30 articles.
1. Integrated LC oscillators for frequency synthesis in wireless applications;Samori;IEEE Commun. Mag.,2002
2. Concepts and methods in optimization of integrated LC VCOs;Ham;IEEE J. Solid-State Circuits,2001
3. Design and performance analysis of low phase noise LC-voltage controlled oscillator;Gurjar;TELKOMNIKA Telecommun. Comput. Electron. Control,2023
4. Comprehensive analysis of LC cross-coupled oscillator with active and passive transconductance amplification structure;Vosta;Analog Integr. Circuits Signal Process.,2023
5. Cramming more components onto integrated circuits;Moore;Electron. Mag.,1965
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. New implementation of analog artificial neural network for breast cancer classification;2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA);2024-02-01