Performance and variability analysis of ALD-grown wafer scale HfO2/Ta2O5-based memristive devices for neuromorphic computing

Here, we report a large-scale wafer microfabrication process and in-depth electrical analysis of atomic layer deposition (ALD) grown bilayer (i.e., HfO 2 /Ta 2 O 5 ) memristive devices. The fabricated bilayer devices initially require an electroforming event and show stable bipolar resistive switching responses with some variations in the device switching voltages. These variations are covered in the 15.7%–22.7% range corresponding to the maximum switching voltage of the tested devices. Moreover, time series analysis (TSA) is employed by considering the device switching voltages ( V SET and V RESET ) to predict the device performance and the obtained outcomes are well matched to the experimental data. Furthermore, the least values of coefficient of variability ( C V ) in the device switching voltages are 6.09% ( V SET ) and 3.22% ( V RESET ) in the case of device-to-device (D2D) while 1.76% ( V SET ) and 2.14% ( V RESET ) in the case of cycle-to-cycle (C2C). Furthermore, the fabricated devices efficiently perform the synaptic functionalities in terms of potentiation (P) and depression (D), paired-pulse facilitation (PPF), and paired-pulse depression (PPD), with a least value of nonlinearity (NL) factor of 0.43 in synaptic response, which is close to the ideal value of NL in biological synapses. Therefore, the present work shows that the single ALD system can be an efficient deposition method to deposit high-k oxide materials for memristive arrays over large-scale wafers.

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Publication Details

Journal
Frontiers in Nanotechnology
Published
2025-06-19
DOI
https://doi.org/10.3389/fnano.2025.1621554
Citations
5
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
3.65

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article

Performance and variability analysis of ALD-grown wafer scale HfO2/Ta2O5-based memristive devices for neuromorphic computing

Deepika Yadav, Spyros Stathopoulos, Sanjay Kumar
5 citations
Frontiers in Nanotechnology
Advanced Memory and Neural Computing
3.65
article

Performance and variability analysis of ALD-grown wafer scale HfO2/Ta2O5-based memristive devices for neuromorphic computing

Deepika Yadav, Spyros Stathopoulos, Sanjay Kumar
article en
5 citations

Abstract

Here, we report a large-scale wafer microfabrication process and in-depth electrical analysis of atomic layer deposition (ALD) grown bilayer (i.e., HfO 2 /Ta 2 O 5 ) memristive devices. The fabricated bilayer devices initially require an electroforming event and show stable bipolar resistive switching responses with some variations in the device switching voltages. These variations are covered in the 15.7%–22.7% range corresponding to the maximum switching voltage of the tested devices. Moreover, time series analysis (TSA) is employed by considering the device switching voltages ( V SET and V RESET ) to predict the device performance and the obtained outcomes are well matched to the experimental data. Furthermore, the least values of coefficient of variability ( C V ) in the device switching voltages are 6.09% ( V SET ) and 3.22% ( V RESET ) in the case of device-to-device (D2D) while 1.76% ( V SET ) and 2.14% ( V RESET ) in the case of cycle-to-cycle (C2C). Furthermore, the fabricated devices efficiently perform the synaptic functionalities in terms of potentiation (P) and depression (D), paired-pulse facilitation (PPF), and paired-pulse depression (PPD), with a least value of nonlinearity (NL) factor of 0.43 in synaptic response, which is close to the ideal value of NL in biological synapses. Therefore, the present work shows that the single ALD system can be an efficient deposition method to deposit high-k oxide materials for memristive arrays over large-scale wafers.

Frontiers in NanotechnologyVol. 7
Indian Institute of Technology Dhanbad (IN), National Microelectronics Institute (GB)
Department of Science and Technology, Ministry of Science and Technology, India, Forskningsrådet om Hälsa, Arbetsliv och Välfärd, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 7%
Advanced Memory and Neural Computing
3.65
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