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Resistive switching phenomenon in carbon film is associated with formationand annihilation of low resistance sp2 nanochannels within the amorphous sp3matrix. The thinnest point of these graphitic nanochannels behaves like quantumwire (QW) and limits current flow. Transport mechanism at these bottlenecks canbe described within the framework of quantum point contact (QPC) model. Themodel applies mesoscopic Landauer formalism and correlates device resistancestate with the density of the nanochannels as well as lateral area of itsconstriction. However, QPC model parameter extraction from macroscopic I-Vcharacteristic is not feasible due to multiple nonlinear and closely coupledparameters, e.g. barrier height ({\Phi}), barrier curvature ({\alpha}) andvoltage drop ratio (\b{eta}). In this work, a hybrid genetic algorithm (GA)based parameter extraction flow is proposed and is applied to extractparameters from experimental result of carbon-based resistive memories. In thisproposed flow, the number of single subband channels for low resistance state(NLRS) and barrier curvature parameter at high resistance state ({\alpha}HRS)were first derived by exploring boundary conditions at low voltage. The fourremaining parameters ({\Phi}HRS, {\Phi}LRS, {\alpha}LRS, and \b{eta}) were thenextracted from experimental I-V characteristic by leveraging hybrid GA. Meanabsolute percentage error (MAPE) of fitted QPC models is only 3.7% and theextracted parameters are within reasonable range. Hereby, we regard the QPCmodel as a viable model to describe conduction mechanism of graphiticnanochannels in carbon-based resistive memories.

Quantum Point Contact Parameter Extraction of Carbon-based Resistive Memory using Hybrid Genetic Algorithm