  Resistive switching phenomenon in carbon film is associated with formation
and annihilation of low resistance sp2 nanochannels within the amorphous sp3
matrix. The thinnest point of these graphitic nanochannels behaves like quantum
wire (QW) and limits current flow. Transport mechanism at these bottlenecks can
be described within the framework of quantum point contact (QPC) model. The
model applies mesoscopic Landauer formalism and correlates device resistance
state with the density of the nanochannels as well as lateral area of its
constriction. However, QPC model parameter extraction from macroscopic I-V
characteristic is not feasible due to multiple nonlinear and closely coupled
parameters, e.g. barrier height ({\Phi}), barrier curvature ({\alpha}) and
voltage drop ratio (\b{eta}). In this work, a hybrid genetic algorithm (GA)
based parameter extraction flow is proposed and is applied to extract
parameters from experimental result of carbon-based resistive memories. In this
proposed 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 four
remaining parameters ({\Phi}HRS, {\Phi}LRS, {\alpha}LRS, and \b{eta}) were then
extracted from experimental I-V characteristic by leveraging hybrid GA. Mean
absolute percentage error (MAPE) of fitted QPC models is only 3.7% and the
extracted parameters are within reasonable range. Hereby, we regard the QPC
model as a viable model to describe conduction mechanism of graphitic
nanochannels in carbon-based resistive memories.
