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Furthermore, extensive research on electrochemical metallization (ECM) memristors as synaptic cells have been carried out toward a linear conductance update for online learning applications. In most cases, however, a conductance distribution change over time has not been studied as a major issue, giving less consideration to inference\u2010only computing accelerators based on offline learning. Herein, organic\u2013inorganic bilayer stacking for synaptic unit cells using poly(1,3,5\u2010trivinyl\u20101,3,5\u2010trimethyl cyclotrisiloxane) (pV3D3) and Al<jats:sub>2<\/jats:sub>O<jats:sub>3<\/jats:sub> thin films is suggested, showing highly enhanced reliability for offline learning. The bilayer structure achieves better reliability and control of the analog resistive switching and synaptic functions, respectively, through the guided formation of conductive filaments via tip\u2010enhanced electric fields. In addition, 5\u2010bit multilevel states achieve long\u2010term stability (&gt;10<jats:sup>4<\/jats:sup>\u2009s) following an in\u2010depth study on conductance\u2010level stability. Finally, a device\u2010to\u2010system\u2010level simulation is performed by building a convolutional neural network (CNN) based on the hybrid devices. This highlighted the significance of multilevel states in fully connected layers. 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