Crystallinity-controlled volatility tuning of ZrO2 memristor for physical reservoir computing

Abstract

Memristors have been emerging as promising candidates for computing systems in post-Moore applications, particularly electrochemical metallization-based memristors, which are poised to play a crucial role in neuromorphic computing and machine learning. These devices are favored for their high integration density, low power consumption, rapid switching speed, and significant on/off ratio. Despite advancements in various materials, achieving adequate electrical performance—characterized by threshold switching (TS) behavior, spontaneous reset, and low off-state resistance—remains challenging due to the limitations in conductance filament control within the nanoscale resistive switching layer. In this study, we introduce an efficient method to control the ZrO₂ crystallinity for tunable volatility memristor by establishing the filament paths through a simple thermal treatment process in a single oxide layer. The effect of ZrO₂ crystallinity to create localized filament paths for enhancing Ag migration and improving TS behavior is also investigated. In contrast to its amorphous counterpart, crystallized ZrO₂ volatile memristor, treated by rapid thermal annealing, demonstrates a steep switching slope (0.21 mV dec^–1), a high resistance state (25 GΩ), and forming-free characteristics. The superior volatile performance is attributed to localized conductive filaments along low-energy pathways, such as dislocations and grain boundaries. By coupling with enhanced volatile switching behavior, we believe that the volatility is finely tuned to function as short-term memory for reservoir computing, making it particularly well-suited for tasks such as audio and image recognition.