High-density crossbar arrays based on storage class memory (SCM) are ideally suited to handle an exponential increase in data storage and processing as a central hardware unit in the era of AI-based technologies. To achieve this, selector devices are required to be co-integrated with SCM to address the sneak-path current issue that indispensably arises in such crossbar-type architecture. In this perspective, we first summarize the current state of tellurium-based threshold-switching devices and recent advances in the material, processing, and device aspects. We thoroughly review the physicochemical properties of elemental tellurium (Te) and representative binary tellurides, their tailored deposition techniques, and operating mechanisms when implemented in two-terminal threshold switching devices. Lastly, we discuss the promising research direction of Te-based selectors and possible issues that need to be considered in advance.

The report reviews recent research efforts in demonstrating a computing system whose operation principle mimics the dynamics of biological neurons. The temporal variation of the membrane potential of neurons is one of the key features that contribute to the information processing in the brain. We first summarize the neuron models that explain the experimentally observed change in the membrane potential. The function of ion channels is briefly introduced to understand such change from the molecular viewpoint. Dedicated circuits that can simulate the neuronal dynamics have been developed to reproduce the charging and discharging dynamics of neurons depending on the input ionic current from presynaptic neurons. Key elements include volatile memristors that can undergo volatile resistance switching depending on the voltage bias. This behavior called the threshold switching has been utilized to reproduce the spikes observed in the biological neurons. Various types of threshold switch have been applied in a different configuration in the hardware demonstration of neurons. Recent studies revealed that the memristor-based circuits could provide energy and space efficient options for the demonstration of neurons using the innate physical properties of materials compared to the options demonstrated with the conventional complementary metal-oxidesemiconductors (CMOS).