In this letter, we propose a method to enhance resistive switching properties in SiCN-based conductive-bridge resistive switching memory (CBRAM) devices by inserting a thin Al2O3 layer between the SiCN resistive switching layer and the TiN bottom electrode. Compared with the Cu/Ta/SiCN/TiN single-layer device, the Cu/Ta/SiCN/Al2O3/TiN double layer device exhibits uniform resistive switching, long stable endurance cycles (>1.6 × 104), and stable retention (104 s) at 125 °C. These substantial improvements in the resistive switching properties are attributed to the location of the formation and rupture of conductive filaments that can be precisely controlled in the device after introducing the Al2O3 layer. Moreover, a multilevel resistive switching characteristic is observed in the Cu/Ta/SiCN/Al2O3/TiN double layer CBRAM device. The distinct six-level resistance states are obtained in double layer devices by varying the compliance current. The highly stable retention characteristics (>104) of the Cu/Ta/SiCN/Al2O3/TiN double layer device with multilevel resistance states are also demonstrated.

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This study investigated an oxygen-vacancy-controlled bilayer TiN/TaOy/TaOx/Pt memristive synaptic device for neuromorphic computing. Multilevel characteristics of the synaptic device were observed with RESET voltage varying between −1.4 and −1.9 V. The device shows highly stable reparative 200 potentiation and depression cycles. The high nonlinearity results of αp = 0.83 for potentiation and αd = −2.03 for depression were observed with the device’s potentiation and depression functions. The device also exhibits a highly stable DC endurance of at least 1000 cycles, an AC pulse endurance of 1 M, and a steady retention of 104 s at 100 °C without any degradation. Furthermore, a Hopfield neural network (HNN) is trained to recognize a 28 × 28 pixels image as an input, representing 784 synapses. In 23 epochs, the HNN successfully identified the input image with training accuracy over 92%. This bilayer memristive device can be highly suitable for neuromorphic devices in the development of neuromorphic-computing field.

In today’s new era, multifunctional devices are most prominent due to their compact design, reduction in operating cost, and reduced need of being limited to single functional devices. The electronic synapses and electro-optic functions of the device are such a cornerstone for neuromorphic computing and image sensing applications. In this work, we fabricate a ZTO-based invisible memristor for simulating the human brain for neuromorphic computing and image sensing applications. Long-term potentiation and depressionat least 790repetitive cycles are observed which ensures the synaptic strength. The first-principles density functional theory calculations give insights into the device’s microscopic charge density distribution and switching mechanism. The experimental potentiation and depression data are used to train the Hopfield neural network (HNN) for image recognition of 28 × 28 pixels comprising 784 synapses. The HNN can be successfully trained to identify the input image with a training accuracy of more than 96% in 17 iterations. Furthermore, the device shows excellent highly stable electrical set and optical reset endurance for at least 1500 cycles without degradation, good retention (104 s) at 90 °C, and high transparency (∼85%). This work not only enables us to use our device in artificial intelligence but also provides a significant advantage in the field of image sensing.