Experimental technique for the performance evaluation and optimization of 1/f noise spectrum investigation in electron devices. (February 2017)

• Record Type: Journal Article
• Authors: Magnone, Paolo; Traverso, Pier Andrea; Fiegna, Claudio
• Is Part Of: Measurement. Volume 98(2017)
• Page Start: 421
• Page End: 428
• Publication Date: 2017-02
• Subjects: 1/f noise -- Flicker noise -- Electron device -- MOSFET -- Non-parametric methods -- Power spectrum -- PSD -- Welch -- Bartlett -- Periodogram; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2016.09.020
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Non-parametric methods for the optimal 1/f noise spectrum estimation are analyzed. Variance of the spectrum is estimated from a single realization. Best performance is achieved with: Welch method; flat top window; 90% of overlap. The smoothing of the correlation is mainly due to the main lobe bandwidth. Abstract: In this paper, a fast and systematic experimental technique is proposed, which is devoted to the performance evaluation of power spectral density non-parametric estimators, in the framework of 1/f-noise bias-varying investigation at the ports of semiconductor electron devices. The methodology, which allows to overcome the need both for cumbersome analytical computation of a-priori moments, and non-reliable and time-consuming conventional statistical inference, provides the performance of the estimator and related parameters under investigation from a single realization, by exploiting the frequency stationarity of non-parametric algorithms in the relative and asymptotical sense. The technique has been applied to the case-study of a power MOSFET, by considering Bartlett, Welch and circular Welch estimators and different time windows and data record segmentation strategies, in order to verify its capabilities and, in particular, to identify the optimal non-parametric estimation of 1/f noise spectrum. In addition, results obtained by means of conventional statistical inference are compared to the estimates provided by the technique proposed, to the aim of further experimental assessment.