Sn-Doped selector-only memory with stable threshold voltage margin for robust binary neural network inference

Abstract With the rise of data-centric applications such as edge AI and neuromorphic computing, there is increasing demand for memory solutions that overcome the limitations of conventional nonvolatile devices. Selector-only memory (SOM), which stores data through threshold voltage (V th ) modulation in chalcogenide-based selector materials, offers a compact and scalable alternative. However, narrow read window margins and V th drift remain major reliability concerns. In this work, we introduce a Sn-doped GeSbSeTe (Sn-GSST) material system that enhances SOM performance by reducing trap depth, increasing the population of shallow band-tail trap states, and widening the V th margin. These improvements enable stable multibit switching and improved endurance. We evaluate the device’s system-level applicability through binary neural network (BNN) inference on the German Traffic Sign Recognition Benchmark (GTSRB) dataset, where V th -induced bit error rate (BER) are modeled using statistical distributions. Sn-GSST devices show significantly lower BER of less than 0.01, leading to improved inference robustness. Finally, Shannon entropy-based error correction code (ECC) analysis confirms that the reduced BER of Sn-GSST leads to lower redundancy overhead and higher inference efficiency. This study demonstrates how material-level engineering can directly translate to system-level reliability and performance in neuromorphic memory applications.