Calculating relaxation time distribution function from power spectrum based on inverse integral transformation method

A novel method is presented for obtaining the distribution function of relaxation times G(tau) from power spectrum 1/f(alpha) (1 <= alpha <= 2). It is derived using McWhorter model and its inverse Stieltjes transform. Unlike the pre-assumed conventional g(tau) distribution, the extracted G(tau) has a peak whose width increases as the slope of the power spectrum alpha decreases. The peak position determines the dominant time constant of the system. Our method is unique because the distribution function is directly extracted from the measured power spectrum. We then demonstrate the validity of this method in the analysis of noise in transistor

Digital implementation of a multilayer perception based on stochastic computing with learning function

Stochastic Computing (SC) [2] is a probability-based computing method, which enables the performance of various operations with a small number of logic gates (i.e., low power) in exchange for high accuracy. Using SC for edge artificial intelligence (AI) integrated circuits can help circumvent the limitations inherent in the power and area required for edge AI. In this study, a three-layered Neural Network (NN) is presented with an online learning function that introduces pseudo-activation, pseudo-subtraction, and imperfect addition into the SC framework. This method may expand the options for edge AI integrated circuits using SC

Detection of discrete surface charge dynamics in GaAs-based nanowire through metal-tip-induced current fluctuation

We investigated the detection of discrete charge dynamics of an electron trap in a GaAs-based nanowire surface through current fluctuation induced by a metallic scanning probe tip. An equivalent circuit model indicated that the charge state in the surface strongly reflects the channel potential when the local surface potential is fixed by the metal tip, which suggests that random charging and discharging dynamics of the trap appears as random telegraph signal (RTS) noise in the nanowire current. Experimental demonstration of the concept was carried out using a GaAs-based nanowire and an atomic force microscope (AFM) system with a conductive tip. We observed the RTS noise in the drain current and superposition of the Lorentzian component in the noise spectrum when the metal tip was in contact with the nanowire surface at specific positions. The obtained results indicate the possibility of detecting charge dynamics of the individual surface trap in semiconductor devices

北極海とオホーツク海におけるヨウ素129の水平分布解析

http://www.godac.jamstec.go.jp/darwin/cruise/mirai/mr19-03c/