Recent Progress in Selector and Self‐Rectifying Devices for Resistive Random‐Access Memory Application

Dongale, Tukaram D.; Kamble, Girish U.; Kang, Dae Yun; Kundale, Somnath S.; An, Hongyu; Kim, Tae Geun

doi:10.1002/pssr.202100199

Cited by 37 publications

(17 citation statements)

References 188 publications

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“…When an external electric field is applied to the device, the Ag electrode oxidizes and produces Ag + ions (Ag → Ag + + e – ) that migrate to the Pt bottom electrode. After reaching the Pt bottom electrode, the Ag+ ions are reduced to Ag atoms and one or more broad conductive filaments are established . Furthermore, the boron vacancies are transported toward the Ag top electrode owing to their net negative charge and participate in the formation of conductive filaments.…”

Section: Resultsmentioning

confidence: 99%

“…After reaching the Pt bottom electrode, the Ag+ ions are reduced to Ag atoms and one or more broad conductive filaments are established. 58 Furthermore, the boron vacancies are transported toward the Ag top electrode owing to their net negative charge 59 and participate in the formation of conductive filaments. As the applied CC is increased from 5 μA to 5 mA, the concentrated/wider conducting filaments attempted to grow in the device (Figure 4d−g).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

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Amorphous Boron Nitride Memristive Device for High-Density Memory and Neuromorphic Computing Applications

Khot

Dongale

Nirmal

et al. 2022

ACS Appl. Mater. Interfaces

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Although two-dimensional (2D) nanomaterials are promising candidates for use in memory and synaptic devices owing to their unique physical, chemical, and electrical properties, the process compatibility, synthetic reliability, and cost-effectiveness of 2D materials must be enhanced. In this context, amorphous boron nitride (a-BN) has emerged as a potential material for future 2D nanoelectronics. Therefore, we explored the use of a-BN for multilevel resistive switching (MRS) and synaptic learning applications by fabricating a complementary metal-oxide-semiconductor (CMOS)-compatible Ag/a-BN/Pt memory device. The redox-active Ag and boron vacancies enhance the mixed electrochemical metallization and valence change conduction mechanism. The synthesized a-BN switching layer was characterized using several analyses. The fabricated memory devices exhibited bipolar resistive switching with low set and reset voltages (+0.8 and −2 V, respectively) and a small operating voltage distribution. In addition, the switching voltages of the device were modeled using a time-series analysis, for which the Holt’s exponential smoothing technique provided good modeling and prediction results. According to the analytical calculations, the fabricated Ag/a-BN/Pt device was found to be memristive, and its MRS ability was investigated by varying the compliance current. The multilevel states demonstrated a uniform resistance distribution with a high endurance of up to 104 direct current (DC) cycles and memory retention characteristics of over 106 s. Conductive atomic force microscopy was performed to clarify the resistive switching mechanism of the device, and the likely mixed electrochemical metallization and valence change mechanisms involved therein were discussed based on experimental results. The Ag/a-BN/Pt memristive devices mimicked potentiation/depression and spike-timing-dependent plasticity-based Hebbian-learning rules with a high pattern accuracy (90.8%) when implemented in neural network simulations.

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Section: Resultsmentioning

confidence: 99%

Section: ■ Results and Discussionmentioning

confidence: 99%

Amorphous Boron Nitride Memristive Device for High-Density Memory and Neuromorphic Computing Applications

Khot

Dongale

Nirmal

et al. 2022

ACS Appl. Mater. Interfaces

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“…[35] The has normally been linked with the Schottky barrier at the electrode/electrolyte interface. [36] For example, self-rectifying behavior was also reported in a Cu/Al 2 O 3 /a-Si/Ta resistive memory where the non-linearity observed on the device I-V characteristic was ascribed to the effective Schottky barrier height at the electrode/ aSi interface. [37] The self-rectifying behavior in this work could be attributed to the rectifying property of the Schottky barrier at the SiC/W interface.…”

Section: Switching Behaviormentioning

confidence: 91%

Back‐End‐of‐Line SiC‐Based Memristor for Resistive Memory and Artificial Synapse

Kapur

Guo

Reynolds

et al. 2022

Adv Elect Materials

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Two‐terminal memristor has emerged as one of the most promising neuromorphic artificial electronic devices for their structural resemblance to biological synapses and ability to emulate many synaptic functions. In this work, a memristor based on the back‐end‐of‐line (BEOL) material silicon carbide (SiC) is developed. The thin film memristors demonstrate excellent binary resistive switching with compliance‐free and self‐rectifying characteristics which are advantageous for the implementation of high‐density 3D crossbar memory architectures. The conductance of this SiC‐based memristor can be modulated gradually through the application of both DC and AC signals. This behavior is demonstrated to further emulate several vital synaptic functions including paired‐pulse facilitation (PPF), post‐tetanic potentiation (PTP), short‐term potentiation (STP), and spike‐rate‐dependent plasticity (SRDP). The synaptic function of learning‐forgetting‐relearning processes is successfully emulated and demonstrated using a 3 × 3 artificial synapse array. This work presents an important advance in SiC‐based memristor and its application in both memory and neuromorphic computing.

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“…Because FTJs require both directions of bias for resistance state variation, only bidirectional selectors are suitable. It has been demonstrated that these selector devices successfully suppress sneak current with a high rectifying ratio (>10 5 ), which is the current ratio of LRS at the read voltage and the opposite direction of the read voltage 142–146 . However, implementing external selectors can degrade the advantages of array configuration and integration density.…”

Section: Neuromorphic Computing Systems Based On Fluorite‐structured ...mentioning

confidence: 99%

Neuromorphic devices based on fluorite‐structured ferroelectrics

Lee

Park

Kim

et al. 2022

InfoMat

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A continuous exponential rise has been observed in the storage and processing of the data that may not curtail in the foreseeable future. The required data processing speed and power consumption are restricted by the buses between the logic and memory devices that are characteristic of the von Neumann computing architecture. Bio-mimicking neuromorphic computing has garnered considerable academic and industrial interest to resolve these challenges.Additionally, devices based on emerging nonvolatile memories capable of mimicking the behaviors of synapses and neurons, which are the main elements in biological computing systems (brains), are attracting significant interest from the device community. With the discovery of ferroelectricity in fluorite-structured oxides, such as HfO 2 and ZrO 2 , which are compatible with the state-of-the-art complementary-metal-oxide-semiconductor processes, ferroelectric devices have rapidly evolved as the main direction of these research and development activities. Fundamental science related to fluorite-structured ferroelectrics has been intensively studied over the last decade. At present, the focus is gradually moving to practical applications, including neuromorphic computing and advanced classical processing or memory units in the conventional von Neumann architecture. However, despite its rapid development, the wealth of recent progress in neuromorphic computing devices based on fluorite-structured ferroelectrics has not been reviewed and systemized. This progress report comprehensively reviews and systemizes the recent progress in artificial synaptic and spiking neuron devices for neuromorphic computing based on fluorite-structured ferroelectrics.

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Recent Progress in Selector and Self‐Rectifying Devices for Resistive Random‐Access Memory Application

Cited by 37 publications

References 188 publications

Amorphous Boron Nitride Memristive Device for High-Density Memory and Neuromorphic Computing Applications

Amorphous Boron Nitride Memristive Device for High-Density Memory and Neuromorphic Computing Applications

Back‐End‐of‐Line SiC‐Based Memristor for Resistive Memory and Artificial Synapse

Neuromorphic devices based on fluorite‐structured ferroelectrics

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