We observe how temperature and compliance currents (CCs) affect the coexistence of nonvolatile resistive memory switching (NVMS) and volatile threshold switching (VTS) in a complementary metal-oxide-semiconductor compatible Cu/SiO_x/p⁺⁺-Si device. NVMS and VTS behaviors are investigated at different temperatures (300 K, 360 K, and 420 K) and different CCs (100 nA–1 mA). In CCs smaller than 1 mA, NVMS and VTS coexist, and as the CC decreases, the frequency of occurrence of VTS tends to increase. The frequency of VTS events increases at higher temperatures. We demonstrate the transient characteristics of the device using a pulse transient for SET, RESET, and volatile switching. These results could be used to artificially mimic the long-term and short-term plasticity of biological synapses using the phenomena of NVMS and VTS.

Early fire detection plays a crucial role in minimizing harm to human life, buildings, and the environment. Traditional fire detection systems struggle with detection in dynamic or complex situations due to slow response and false alarms. Conventional systems are based on smoke, heat, and gas sensors, which often trigger alarms when a fire is in full swing. In order to overcome this, a promising approach is the development of memristor-based gas sensors, known as gasistors, which offer a lightweight design, fast response/recovery, and efficient miniaturization. Recent studies on gasistor-based sensors have demonstrated ultrafast response times as low as 1–2 s, with detection limits reaching sub-ppm levels for gases such as CO, NH₃, and NO₂. Enhanced designs incorporating memristive switching and 2D materials have achieved a sensitivity exceeding 90% and stable operation across a wide temperature range (room temperature to 250 °C). This review highlights key factors in early fire detection, focusing on advanced sensors and their integration with IoT for faster, and more reliable alerts. Here, we introduce gasistor technology, which shows high sensitivity to fire-related gases and operates through conduction filament (CF) mechanisms, enabling its low power consumption, compact size, and rapid recovery. When integrated with machine learning and artificial intelligence, this technology offers a promising direction for future advancements in next-generation early fire detection systems.

In this Letter, the self-rectifying resistive switching (RS) behavior is demonstrated in Ti/ZrN/Pt/p-Si resistive random access memory (RRAM) devices. Compared to an RS characteristic of the conventional Ti/ZrN/Pt structures, the memory cell with a p-Si bottom layer shows a larger current ratio. However, a current-limited region is also more clearly obtained in a low voltage region, which can result in one diode-type RRAM with self-selecting properties. Consequently, these results infer that the proposed ZrN-based RRAM cells with a Pt/p-Si selector warrant the realization of the self-selecting RRAM cell without any additional peripheral elements to suppress a disturbance in the reading operation.