Multi-state nonvolatile capacitances in HfO2-based ferroelectric capacitor for neuromorphic computing

Author:

Wu Shuyu123ORCID,Zhang Xumeng24ORCID,Cao Rongrong5ORCID,Zhou Keji2ORCID,Lu Jikai2ORCID,Li Chao13ORCID,Yang Yang1ORCID,Shang Dashan13ORCID,Wei Yingfen24ORCID,Jiang Hao24ORCID,Liu Qi24ORCID

Affiliation:

1. Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics Chinese Academy of Sciences 1 , Beijing 100029, China

2. State Key Laboratory of Integrated Chips and Systems, Frontier institute of Chip and System, Fudan University 2 , Shanghai 200433, China

3. University of Chinese Academy of Sciences 3 , Beijing 101408, China

4. Zhangjiang Fudan International Innovation Center, Fudan University 4 , Shanghai 200433, China

5. The College of Electronic Science and Technology, National University of Defense Technology 5 , Changsha 410073, China

Abstract

In the last decade, HfO2-based ferroelectric capacitors (FeCaps) have undergone significant advancements, particularly within the realm of nonvolatile ferroelectric random access memories (FeRAMs). Nonetheless, the READ operation in FeRAMs is inherently destructive, rendering it unsuitable for neuromorphic computing. In this study, we have engineered tunable nonvolatile capacitances within FeCaps, featuring nondestructive readout functionality. Robust capacitance states can be read at a zero d.c. bias (Vbias) with different a.c. signals, not only preventing the alteration of their stored state but also benefiting to the low power consumption. Moreover, the capacitance memory window (CMW) at Vbias of zero can be effectively modulated through electrode engineering, leading to a larger CMW when there is a greater disparity in work functions between the electrodes. Furthermore, we provide a comprehensive investigation into synaptic behavior of TiN/Hf0.5Zr0.5O2/Pt FeCaps, demonstrating their excellent cycle-to-cycle uniformity, retention, and endurance characteristics, which confirm their high reliability in maintaining nonvolatile capacitance states. These findings underscore the significant potential of FeCaps in advancing low-power neuromorphic computing.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

AIP Publishing

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