In this work, organic material-based resistive switching mechanisms were studied by using graphene oxide as the switching layer. With the insertion of a charge trapping graphene layer, the device showed good stability and good electrical bipolar switching properties, with an ON/OFF ratio about 102–103. The device gradually shifted toward complementary switching behavior while maintaining an ON/OFF ratio of ∼102 from bipolar switching behavior after a specific number of consecutive DC switching cycles with increases in the SET-RESET voltage. The conduction mechanisms for bipolar (P–F conduction) and the complementary switching were verified based on the electrical characteristics and curve fittings. Rapid increases in the injected electrons due to increased voltage in complementary switching facilitated the formation of an intermediate charge reservoir region that, in turn, enhanced performance. The device showed a retention period as high as 104 s at 85 °C and good DC endurance. The device is also capable of multi-resistance states to obtain multi-bit (4-bit) data storage, leading to high density memory realization.

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An artificial synaptic device with continuous conductance variation is essential for the hardware implementation of bioinspired neuromorphic systems. With increasing technological advancement, wearable flexible technology is gaining enormous importance in the research community. High temperature is one of the key issues in flexible technology from the fabrication and applicability aspects. In this work, we have demonstrated the performance of a complementary metal− oxide−semiconductor (CMOS)-compatible high-k material (HfO 2 ) based flexible heterogeneous stacked resistive switching device in an artificial neural network. The device exhibited an excellent memory window (I ON /I OFF ) of around 1.2 × 10 4 with an ultralow variation (σ HRS ∼ 3.95 × 10 −10 S) at 500 μA current compliance. The device shows excellent mechanical and electrical stability for more than 500 DC cycles and data retention capability at 120 °C for more than 10 4 s without any degradation. The device can be used for multilevel cell (MLC) operation in six distinct states and can be useful for the implementation of 2-bit data storage applications. The conduction mechanism in the device was dominated by Schottky emission at the lower field region and hopping conduction at the higher field region of the high-resistance state (HRS), whereas ohmic conduction was satisfied at the lowresistance state (LRS). We have trained the device in the neural network with 96.07% accuracy as the baseline and observed the effect of conductance variation and high-temperature operation. We trained the device at a high temperature with a 95.68% off-chip training accuracy and observed the accuracy profile throughout the time. The device also possessed an excellent mechanical stability under a long-term bending stress (r = 8 mm) of over 10 4 s with an intact memory window. The neural accuracy was measured every 30 min with a maximum of 96.01% to observe the effect of long-term mechanical stress on the off-chip learning process.