Bias dependent variability of low-frequency noise in single-layer graphene FETs. Issue 11 (30th October 2020) - Mavredakis, Nikolaos; Cortadella, Ramon Garcia; Illa, Xavi; Schaefer, Nathan; Calia, Andrea Bonaccini; Anton-Guimerà-Brunet,; Garrido, Jose A.; Jiménez, David
Abstract : Low-frequency noise variability is for the first time examined in single-layer graphene transistors while an analytical compact model demonstrating an outstanding performance is proposed. Abstract : Low-frequency noise (LFN) variability in graphene transistors (GFETs) is for the first time researched in this work under both experimental and theoretical aspects. LFN from an adequate statistical sample of long-channel solution-gated single-layer GFETs is measured in a wide range of operating conditions while a physics-based analytical model is derived that accounts for the bias dependence of LFN variance with remarkable performance. LFN deviations in GFETs stem from the variations of the parameters of the physical mechanisms that generate LFN, which are the number of traps ( N tr ) for the carrier number fluctuation effect (Δ N ) due to trapping/detrapping process and the Hooge parameter ( α H ) for the mobility fluctuations effect (Δ μ ). Δ N accounts for an M-shape of normalized LFN variance versus gate bias with a minimum at the charge neutrality point (CNP) as it was the case for normalized LFN mean value while Δ μ contributes only near the CNP for both variance and mean value. Trap statistical nature of the devices under test is experimentally shown to differ from classical Poisson distribution noticed at silicon-oxide devices, and this might be caused both by the electrolyte interface in GFETs under study and by the premature stage of the GFET technology development which could permit external factors to influence the performance. This not fully advanced GFET process growth might also cause pivotal inconsistencies affecting the scaling laws in GFETs of the same process.