Improved Performance of HfxZnyO‐Based RRAM and its Switching Characteristics down to 4 K Temperature. (29th January 2023) - Lan, Jun; Li, Zhixiong; Chen, Zhenjie; Zhu, Quanzhou; Wang, Wenhui; Zaheer, Muhammad; Lu, Jiqing; Liang, Jinxuan; Shen, Mei; Chen, Peng; Chen, Kai; Zhang, Guobiao; et al.
Abstract: The search for high‐performance resistive random‐access memory (RRAM) devices is essential to pave the way for highly efficient non‐Von Neumann computing architecture. Here, it is reported on an alloying approach using atomic layer deposition for a Zn‐doped HfOx ‐based resistive random‐access memory (HfZnO RRAM), with improved performance. As compared with HfOx RRAM, the HfZnO RRAM exhibits reduced switching voltages (>20%) and switching energy (>3×), as well as better uniformity both in voltages and resistance states. Furthermore, the HfZnO RRAM exhibits stable retention exceeding 10 years, as well as write/erase endurance exceeding 10 5 cycles. In addition, excellent linearity and repeatability of conductance tuning can be achieved using the constant voltage pulse scheme, achieving ≈90% accuracy in a simulated multi‐layer perceptron network for the recognition of modified national institute of standards and technology database handwriting. The HfZnO RRAM is also characterized down to the temperature of 4 K, showing functionality and the elucidation of its carrier conduction mechanism. Hence, a potential pathway for doped‐RRAM to be used in a wide range of temperatures including quantum computing and deep‐space exploration is shown. Abstract : Here, a Zn‐doped HfOx ‐based resistive random‐access memory (RRAM) is fabricated using the atomic layer deposition method. The newly developed RRAM shows improved performance in switching voltage, power consumption, and uniformity. The consecutively controllable states as well as switching characteristics down to 4 K confirm the potential of the RRAM for neuromorphic computation and cryogenic applications.