Analysis of electric current noise in nano-transistors using visibility graphs

Complex network time-series analysis by the Visibility Graph (VG) method is applied to an experimental set of drain current signals from nano-transistor devices (nano-MOSFETs). Electric current in nano-MOSFETs has noisy fluctuations produced by different physical mechanisms, including thermal, electron-hole recombination, and the effect of ion traps present at the gate region. The combination of these mechanisms results in a complex power spectrum, which may contain "corners" at one or more critical frequencies, switching from the well-known 1/f "flicker" noise to 1/f 2 "Brownian" tails, or contain flat regions at low frequencies. More recent studies have shown that current fluctuations in a fully depleted nano-MOSFET may contain low-dimensional chaotic dynamics with critical intermittent behavior. Consequently, noisy current signals in nano-MOSFETs constitute an excellent testbed to assess the ability of the VG method in capturing and discerning subtle features of dynamics under complex scenarios. Using graph metrics, such as clustering coefficient, assortativity, and the rich-club coefficient, we show that the VG structure consistently discerns differences in the nature of noise between fresh and stressed (faulty) transistors and between stochastic and low-dimensional chaotic dynamics. Using three types of surrogate time series, we show that these differences are statistically significant. Moreover, we show that graph metrics other than the degree distribution are crucial in capturing features of complex system dynamics that are mixtures of various types of noise and possibly deterministic chaos. In the case of noisy signals from nano-devices, this has a direct application to device classification and fault detection.