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Hybrid-FE-Layer FeFET With High Linearity and Endurance Toward On-Chip CIM by Array Demonstration

Yuejia Zhou, Hanyong Shao, Runteng Zhu, Wenpu Luo, Weiqin Huang, Linbo Shan, Ru Huang, Kechao Tang

2023IEEE Electron Device Letters22 citationsDOI

Abstract

Analog weight cells based on ferroelectric field-effect transistors (FeFETs) are promising for fast and energy efficient compute-in-memory (CIM) accelerators, yet their on- chip training is hindered by the limited linearity and endurance. To address this critical issue, we introduced a novel FeFET design featuring a hybrid ferroelectric layer of Hf0.5Zr0.5O2 and Hf0.95Al0.05O2 with Al2O3 interlayers. The proposed FeFET achieves a high linearity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha _{\text {p}}= -0.48$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha _{\text {d}}= -1.73$ </tex-math></inline-formula> , good endurance of over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{{9}}$ </tex-math></inline-formula> cycles, and a fast switching speed of ~50 ns. The mechanism of such improvement was probed carefully by comparison with control devices. The benefit of device optimization was experimentally demonstrated by performing an on- chip training task on a 1T NOR array, and the system-level benchmarking of the CIM accelerator based on our FeFET shows an overall superior performance. This work contributes to the development of FeFETs for on- chip neuromorphic hardware.

Topics & Concepts

ChipNotationLinearityComputer scienceMaterials scienceAlgorithmMathematicsElectronic engineeringElectrical engineeringEngineeringArithmeticFerroelectric and Negative Capacitance DevicesAdvanced Memory and Neural ComputingSemiconductor materials and devices