In the quest to create computing systems that mimic the brain’s remarkable ability to process data efficiently and in parallel, researchers have recognized the need for in-memory computing devices that can overcome the limitations of the traditional von Neumann architecture. Recently, neuromorphic devices like memristors and synaptic transistors have been developed to emulate the process of neurons. However, memristors face challenges in simultaneously handling signal transmission and learning tasks. As an alternative, synaptic transistors have gained attention owing to their adjustable weight and improved non-volatile synaptic functionality. Notably, synaptic transistors with electrolyte insulating layers can form electric double layers (EDLs) with high capacitance per unit area, because of mobile ions and dipoles. This enables efficient gate-controlled synaptic behavior. Additionally, amorphous oxynitride semiconductors, used as n-type channels, exhibit superior mobility compared to other materials like 2D transition metals, perovskites, organic materials, and oxides. In our investigation, therefore, we employed a cross-linked poly-4-vinyl phenol (PVP) gate insulator to enhance the synaptic properties of oxynitride thin film transistors.In our study, we employed zinc oxynitride (ZnON) as the channel material. ZnON offers greater stability compared to oxide materials due to its reduced oxygen vacancy concentration. We precisely controlled the solution composition and the curing process during the deposition of the electrolyte insulating layer. To optimize the formation of electric double layers (EDLs), we varied the weight ratio of poly(melamine-co-formaldehyde) methylated (PMF) and poly-4-vinyl phenol (PVP). Our investigation focused on synaptic properties, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), long-term memory, and high-band filtering. All samples exhibited a counter-clockwise hysteresis curve and a memory window exceeding 2 V at 1 V of drain voltage (Vd), except for the PVP:PMF 2:1 sample. Notably, the device with a 10:1 weight ratio demonstrated the most effective EPSC response, showing a gradual increase in EPSC with repeated gate voltage (Vg) pulses. Additionally, we analyzed the EPSC characteristics under various Vg pulse conditions (width, frequency, period). To understand the behavior of internal ions and dipoles in the cross-linked PVP insulating layer, we measured the frequency-dependent capacitance. Interestingly, the capacitance decreased as the frequency increased, suggesting that internal ions could be easily influenced by electrical bias in the cross-linked PVP electrolyte, forming an EDL layer. These findings suggest that the cross-linked PVP/ZnON-based transistor could potentially serve as a significant synaptic transistor component in artificial intelligence technology.
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