Abstract
Many efforts have been reported to improve the linear conductance modulation of analog RRAM for neuromorphic computing, while the related model for analog switching is still lacking. In this letter, we propose a physics-based analytic model to account for analog behavior of HfO2-based RRAM. The switching from abrupt SET process to gradual SET is analyzed quantitatively by considering the electrical and thermal effects of intermediate modulation layer (IML). The proposed model well describes the conductance update in potentiation and depression, which is validated with the experimental data. The results show that for the case of introducing IML to improve linearity, the electrical resistivity of IML has a greater effect than thermal conductivity on the control of oxygen vacancy generation rate, thus playing a dominant role in the nonlinearity optimization of analog RRAM.
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