A Fractional Drift Diffusion Model for Organic Semiconductor Devices

Because charge carriers of many organic semiconductors(OSCs)exhibit fractional drift diffusion(Fr-DD)transport properties,the need to develop a Fr-DD model solver becomes more apparent.However,the current research on solving the governing equations of the Fr-DD model is practically nonexistent.In this paper,an iterative solver with high precision is developed to solve both the transient and steady-state Fr-DD model for organic semiconductor devices.The Fr-DD model is composed of two fractionalorder carriers(i.e.,electrons and holes)continuity equations coupled with Poisson’s equation.By treating the current density as constants within each pair of consecutive grid nodes,a linear Caputo’s fractional-order ordinary differential equation(FrODE)can be produced,and its analytic solution gives an approximation to the carrier concentration.The convergence of the solver is guaranteed by implementing a successive over-relaxation(SOR)mechanism on each loop of Gummel’s iteration.Based on our derivations,it can be shown that the Scharfetter–Gummel discretization method is essentially a special case of our scheme.In addition,the consistency and convergence of the two core algorithms are proved,with three numerical examples designed to demonstrate the accuracy and computational performance of this solver.Finally,we validate the Fr-DD model for a steady-state organic field effect transistor(OFET)by fitting the simulated transconductance and output curves to the experimental data.

An improved memristor model for brain-inspired computing

Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover,the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.

Comparative Methodical Assessment of Established MOSFET Threshold Voltage Extraction Methods at 10-nm Technology Node

Threshold voltage (VTH) is the most evocative aspect of MOSFET operation. It is the crucial device constraint to model on-off transition characteristics. Precise VTH value of the device is extracted and evaluated by several estimation techniques. However, these assessed values of VTH diverge from the exact values due to various short channel effects (SCEs) and non-idealities present in the device. Numerous prevalent VTH extraction methods are discussed. All the results are verified by extensive 2-D TCAD simulation and confirmed through analytical results at 10-nm technology node. Aim of this research paper is to explore and present a comparative study of largely applied threshold extraction methods for bulk driven nano-MOSFETs especially at 10-nm technology node along with various sub 45-nm technology nodes. Application of the threshold extraction methods to implement noise analysis is briefly presented to infer the most appropriate extraction method at nanometer technology nodes.