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Ferroelectric Hf0.5Zr0.5O2 for Analog Memory and In-Memory Computing Applications down to Deep Cryogenic Temperatures
  • +2
  • Heorhii Bohuslavskyi,
  • Kestutis Grigoras,
  • Mário Ribeiro,
  • Mika Prunnila,
  • Sayani Majumdar
Heorhii Bohuslavskyi
VTT Technical Research Centre of Finland Ltd
Author Profile
Kestutis Grigoras
VTT Technical Research Centre of Finland Ltd
Mário Ribeiro
VTT Technical Research Centre of Finland Ltd
Mika Prunnila
VTT Technical Research Centre of Finland Ltd
Sayani Majumdar
Information Technology and Communication Sciences, Tampere University, VTT Technical Research Centre of Finland Ltd

Corresponding Author:

Abstract

Low-power non-volatile memories operating down to deep cryogenic temperatures are important for a large spectrum of applications from high-performance computing and electronics interfacing quantum computing hardware to space-based electronics. Despite the potential of Hf0.5Zr0.5O2 (HZO), thanks to its compatibility with complementary metal-oxide-semiconductor (CMOS) back-end-of line processing, only a few studies of HZO based memory devices down to cryogenic operation temperatures exist. Here, we report on analog ferroelectric memory stack fabrication with 10 nm HZO and its detailed characterization under a wide range of pulse amplitudes and frequencies from 300 K down to 4 K. When operated at temperatures below 100 K, HZO devices can support high amplitude voltage pulses, yielding record high remnant polarization Pr of up to 75µC cm-2 at ±7 Vp (14 Vpp) pulse amplitudes accompanied with frequency-dependent memory window between 6 and 8 V. Devices show excellent endurance exceeding 109 cycles of ±5 Vp (10 Vpp) and Pr of 30 µC cm-2 without significant degradation of coercive voltages or loss of polarization at cryogenic temperatures. At least 20 reproducible analog states for temperatures below 100 K with almost ideal linearity of intermediate polarization states in both pulse directions are observed, demonstrating the high potential of analog cryogenic ferroelectric memories, essential for online training in In-Memory-Computing architecture.
15 Apr 2024Submitted to TechRxiv
18 Apr 2024Published in TechRxiv