Scalable and sustainable N-Si-Ge-Te Ovonic threshold switching devices for energy-efficient artificial neuron applications

Neuromorphic computing, inspired by biological nervous systems, yields high energy efficiency and data throughput by integrating computation and storage within memory crossbar arrays. A key requirement for neuromorphic hardware is an artificial neuron capable of low-power, high-frequency operation. Ovonic threshold switch (OTS) devices have attracted attention due to their scalability and intrinsic capacitance, enabling simple circuitry to demonstrate leaky integrate-and-fire (LIF) behavior. This study proposes a sustainable and scalable OTS device fabricated with non-toxic, industry-friendly materials and provides insights into the roles of individual elements in bond formation, correlating with enhanced electrical performance (J _off = 2.3 ∙ 10 ^− 8 MA/cm²). Finally, our optimized NSGT OTS device demonstrates low-power spiking operation, achieving 0.56 pJ/µm ² per spike. These findings establish stoichiometric guidelines for designing high-performance Te-based OTS devices for energy-efficient neuromorphic computing.