Low Frequency Noise Variability Research Articles

State-of-the-art low frequency noise performance of n-MOSFET for analog/RF applications

In this paper, we present the results of low frequency noise (LFN) characterization of n-MOSFET. We are going to show that the device treated with the new anneal step at the end of the process shows outstanding LFN performance. Results demonstrate that the new implemented process change can improve the noise level up to ∼6x. The LFN variability of the device under test improved up to ∼2x.

Comprehensive analysis of low-frequency noise variability components in bulk and fully depleted silicon-on-insulator metal–oxide–semiconductor field-effect transistor

The low-frequency noise (LFN) variability in bulk and fully depleted silicon-on-insulator (FDSOI) metal–oxide–semiconductor field-effect transistor (MOSFET) with silicon on thin box (SOTB) technology was investigated. LFN typically shows a flicker noise component and a signal Lorentzian component by random telegraph noise (RTN). At a weak inversion state, the random dopant fluctuation (RDF) in a channel is strongly affected to not only RTN variability but also flicker noise variability in the bulk MOSFET compared with SOTB MOSFET because of local carrier number fluctuation in the channel. On the other hand, the typical level of LFN in SOTB MOSFET is slightly larger than that in the bulk MOSFET because of an additional interface on the buried oxide layer. However, considering the tailing characteristics of LFN variability, LFN in SOTB MOSFET can be assumed to be smaller than that in the bulk MOSFET, which enables the low-voltage operation of analog circuits.

Charge-Based Compact Model for Bias-Dependent Variability of 1/ $f$ Noise in MOSFETs

Variability of low frequency noise (LFN) in MOSFETs is bias-dependent. Moderate- to large-sized transistors commonly used in analog/RF applications show 1/f-like noise spectra, resulting from the superposition of random telegraph noise (RTN). Carrier number and mobility fluctuations are considered as the main causes of low frequency noise. While their effect on the bias-dependence of LFN has been well investigated, the way these noise mechanisms contribute to the bias-dependence of variability of LFN has been less well understood. LFN variability has been shown to be maximized in weak inversion (sub-threshold), while increased drain bias also increases LFN variability. However, no compact model has been proposed to explain this bias-dependence in detail. In combination with the charge-based formulation of LFN, the present paper proposes a new model for bias-dependence of LFN variability. Comparison with experimental data from moderately-sized NMOS and PMOS transistors at all bias conditions provides insight into how carrier number and mobility fluctuation mechanisms impact the bias-dependence of LFN variability.

Low frequency noise variability in ultra scaled FD-SOI n-MOSFETs: Dependence on gate bias, frequency and temperature

In this paper, a parametric statistical analysis of the low-frequency noise (LFN) in very small area (W·L≈10−3μm2) 14nm fully depleted silicon-on-insulator (FD-SOI) n-MOS devices is presented. It has been demonstrated that the LFN origin is due to carrier trapping/detrapping into gate dielectric traps near the interface and the mean noise level in such small area MOSFETs is well approached by the carrier number fluctuations model in all measurement conditions. The impact of gate voltage bias and temperature on the LFN variability, as well as the standard deviation dependence on frequency have been studied for the first time, focusing on their relation to the Random Telegraph Noise (RTN) effect and its characteristics.

Impact of low‐frequency noise variability on statistical parameter extraction in ultra‐scaled CMOS devices

The impact on the extracted low-frequency noise (LFN) parameter values due to LFN variability in CMOS devices is investigated. First, it is demonstrated that the noise level dispersion follows a log normal statistical distribution. Then, based on this feature, it is explained why the mean values from the linear data are different from the mean values (or median values) calculated from the log noise data. Finally, the consequence of this finding in terms of LFN characterisation issues and Monte Carlo LFN variability circuit simulation is discussed.