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Koh EK, Dananjaya PA, Poh HY, Liu L, Lee CXX, Thong JR, You YS, Lew WS. Unraveling the origins of the coexisting localized-interfacial mechanism in oxide-based memristors in CMOS-integrated synaptic device implementations. Nanoscale Horiz 2024; 9:828-842. [PMID: 38450438 DOI: 10.1039/d3nh00554b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The forefront of neuromorphic research strives to develop devices with specific properties, i.e., linear and symmetrical conductance changes under external stimuli. This is paramount for neural network accuracy when emulating a biological synapse. A parallel exploration of resistive memory as a replacement for conventional computing memory ensues. In search of a holistic solution, the proposed memristive device in this work is uniquely poised to address this elusive gap as a unified memory solution. Opposite biasing operations are leveraged to achieve stable abrupt and gradual switching characteristics within a single device, addressing the demands for lower latency and energy consumption for binary switching applications, and graduality for neuromorphic computing applications. We evaluated the underlying principles of both switching modes, attributing the anomalous gradual switching to the modulation of oxygen-deficient layers formed between the active electrode and oxide switching layer. The memristive cell (1R) was integrated with 40 nm transistor technology (1T) to form a 1T-1R memory cell, demonstrating a switching speed of 50 ns with a pulse amplitude of ±2.5 V in its forward-biased mode. Applying pulse trains of 20 ns to 490 ns in the reverse-biased mode exhibited synaptic weight properties, obtaining a nonlinearity (NL) factor of <0.5 for both potentiation and depression. The devices in both modes also demonstrated an endurance of >106 cycles, and their conductance states were also stable under temperature stress at 85 °C for 104 s. With the duality of the two switching modes, our device can be used for both memory and synaptic weight-storing applications.
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Affiliation(s)
- Eng Kang Koh
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Putu Andhita Dananjaya
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Han Yin Poh
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Lingli Liu
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Calvin Xiu Xian Lee
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Jia Rui Thong
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Young Seon You
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Wen Siang Lew
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
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Fang H, Wang J, Nie F, Zhang N, Yu T, Zhao L, Shi C, Zhang P, He B, Lü W, Zheng L. Giant Electroresistance in Ferroelectric Tunnel Junctions via High-Throughput Designs: Toward High-Performance Neuromorphic Computing. ACS Appl Mater Interfaces 2024; 16:1015-1024. [PMID: 38156871 DOI: 10.1021/acsami.3c13171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Ferroelectric tunnel junctions (FTJs) have been regarded as one of the most promising candidates for next-generation devices for data storage and neuromorphic computing owing to their advantages such as fast operation speed, low energy consumption, convenient 3D stack ability, etc. Here, dramatically different from the conventional engineering approaches, we have developed a tunnel barrier decoration strategy to improve the ON/OFF ratio, where the ultrathin SrTiO3 (STO) dielectric layers are periodically mounted onto the BaTiO3 (BTO) ferroelectric tunnel layer using the high-throughput technique. The inserted STO enhances the local tetragonality of the BTO, resulting in a strengthened ferroelectricity in the tunnel layer, which greatly improves the OFF state and reduces the ON state. Combined with the optimized oxygen migration, which can further manipulate the tunneling barrier, a record-high ON/OFF ratio of ∼108 has been achieved. Furthermore, utilizing these FTJ-based artificial synapses, an artificial neural network has been simulated via back-propagation algorithms, and a classification accuracy as high as 92% has been achieved. This study screens out the prominent FTJ by the high-throughput technique, advancing the tunnel layer decoration at the atomic level in the FTJ design and offering a fundamental understanding of the multimechanisms in the tunnel barrier.
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Affiliation(s)
- Hong Fang
- Functional Materials and Acousto-Optic Instruments Institute, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Jie Wang
- Functional Materials and Acousto-Optic Instruments Institute, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Fang Nie
- School of Physics, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China
| | - Nana Zhang
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Tongliang Yu
- School of Physics, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China
| | - Le Zhao
- School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Chaoqun Shi
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Peng Zhang
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Bin He
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Weiming Lü
- Functional Materials and Acousto-Optic Instruments Institute, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Limei Zheng
- School of Physics, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China
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Abstract
Resistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make use of various filament-based oxide materials for creating a channel that is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stresses when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in RRAM for neural computing. The study presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices.
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Affiliation(s)
| | | | | | - Alex James
- Digital University, Thiruvananthapuram, Kerala, India
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Lee J, Jeong BH, Kamaraj E, Kim D, Kim H, Park S, Park HJ. Light-enhanced molecular polarity enabling multispectral color-cognitive memristor for neuromorphic visual system. Nat Commun 2023; 14:5775. [PMID: 37723149 PMCID: PMC10507016 DOI: 10.1038/s41467-023-41419-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 09/01/2023] [Indexed: 09/20/2023] Open
Abstract
An optoelectronic synapse having a multispectral color-discriminating ability is an essential prerequisite to emulate the human retina for realizing a neuromorphic visual system. Several studies based on the three-terminal transistor architecture have shown its feasibility; however, its implementation with a two-terminal memristor architecture, advantageous to achieving high integration density as a simple crossbar array for an ultra-high-resolution vision chip, remains a challenge. Furthermore, regardless of the architecture, it requires specific material combinations to exhibit the photo-synaptic functionalities, and thus its integration into various systems is limited. Here, we suggest an approach that can universally introduce a color-discriminating synaptic functionality into a two-terminal memristor irrespective of the kinds of switching medium. This is possible by simply introducing the molecular interlayer with long-lasting photo-enhanced dipoles that can adjust the resistance of the memristor at the light-irradiation. We also propose the molecular design principle that can afford this feature. The optoelectronic synapse array having a color-discriminating functionality is confirmed to improve the inference accuracy of the convolutional neural network for the colorful image recognition tasks through a visual pre-processing. Additionally, the wavelength-dependent optoelectronic synapse can also be leveraged in the design of a light-programmable reservoir computing system.
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Affiliation(s)
- Jongmin Lee
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea
| | - Bum Ho Jeong
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea
| | - Eswaran Kamaraj
- Department of Chemistry, Kongju National University, Kongju, 32588, Republic of Korea
| | - Dohyung Kim
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea
| | - Hakjun Kim
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea
| | - Sanghyuk Park
- Department of Chemistry, Kongju National University, Kongju, 32588, Republic of Korea.
| | - Hui Joon Park
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea.
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea.
- Hanyang Institute of Smart Semiconductor, Seoul, 04763, Republic of Korea.
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