Dilute magnetic semiconductor electrode based all semiconductor magnetic tunnel junction for high-temperature applications

Malik, Gul Faroz A.; Kharadi, Mubashir A.; Khanday, Farooq Ahmad; Shah, Khurshed A.; Mittal, Sparsh; Kaushik, Brajesh Kumar; Najar, Feroz A.

doi:10.1016/j.physb.2021.413525

Cited by 5 publications

(9 citation statements)

References 31 publications

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“…A InP‐based MTJ was reported with a high TMR ratio of 1.97 × 10 4 %, which is higher than all previous devices. [ 62 ] Figure 4b shows the different DOS of spin‐up and spin‐down electrons in the GaN layers. A graphene‐based MTJ even showed a magnetoresistance ratio of 5.16 × 10 4 % (Figure 4d).…”

Section: Memristor Switching Mechanismsmentioning

confidence: 99%

“…Reported TMR ratio from 1995 to 2022 for MTJs with Al 2 O 3 barriers, [ 69,83–87 ] MgO barriers, [ 75,88–95 ] and nonoxide barriers. [ 62,64,79,96–104 ] …”

Section: Memristor Switching Mechanismsmentioning

confidence: 99%

“…[69,[83][84][85][86][87] MgO barriers, [75,[88][89][90][91][92][93][94][95] and nonoxide barriers. [62,64,79,[96][97][98][99][100][101][102][103][104] junctions. An FTJ is a two-terminal device with an ultrathin ferroelectric layer in the middle and asymmetric conductive layers (usually metals or semiconductors) on both sides.…”

Section: Ferroelectric Tunnel Junctionmentioning

confidence: 99%

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Essential Characteristics of Memristors for Neuromorphic Computing

Chen

Song

Wang

et al. 2022

Adv Elect Materials

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increasingly problematic. Unlike the Von Neumann computing platform, the human brain relies on neurons and synapses for storage and computation, which do not have clear boundaries between them. Therefore, nanodevices that mimic synapses, for high-efficiency computing, have been investigated; among these nanodevices, memristors have attracted most attention because of their low power consumption, high integration density, and the ability to simulate synaptic plasticity, which meet the standards of neuromorphic computing. [4] The first report on the resistive switching phenomenon dates back to the 1960s; [5] since early theories were insufficient to explain this phenomenon, research had been done on it. It was not until the memristor was theoretically proposed in 1971, that the mechanisms underpinning the resistive switching became abundant. [6] The first memristor was manufactured by Hewlett-Packard in 2008. [7] Since then, memristors made of diverse materials have been successfully studied, including conductive filament memristors, magnetic tunnel junctions, ferroelectric tunnel junctions, phase-change memristors, and so on (Figure 1). These devices have been used for storage and computing purposes. [8,9] In recent years, there have been many reviews investigating neuromorphic computing from the perspectives of device electrical properties, [9,10] resistive switching materials, [11,12] memristive synapses and neurons, [13] algorithm optimization, [14] and circuit design. [15] Different from the existing literature, we discuss the possibility of achieving brain-like computing from the perspective of memristor technology and review the establishment of spiking neural network neuromorphic computing systems. In this article, we first review the resistive switching mechanisms of different types of memristors and focus on factors, which affect device stability and the corresponding optimization measures that have been applied. Furthermore, we study the stochasticity, power consumption, switching speed, retention, endurance, and other properties of memristors, which are the basis for neuromorphic computing implementations. We then review various memristor-based neural networks and the building of spike neural network neuromorphic computing systems. Finally, we shed light upon the major challenges and offer our perspectives and opinions for memristor-based brainlike computing systems.The memristor is a resistive switch where its resistive state is programable based on the applied voltage or current. Memristive devices are thus capable of storing and computing information simultaneously, breaking the Von Neumann bottleneck. Since the first nanomemristor made by Hewlett-Packard in 2008, advances so far have enabled nanostructured, low-power, high-durability devices that exhibit superior performance over conventional CMOS devices. Herein, the development of memristors based on different physical mechanisms is reviewed. In particular, device stability, integration density, power consumption, switching speed, retention, and e...

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Section: Memristor Switching Mechanismsmentioning

confidence: 99%

“…Reported TMR ratio from 1995 to 2022 for MTJs with Al 2 O 3 barriers, [ 69,83–87 ] MgO barriers, [ 75,88–95 ] and nonoxide barriers. [ 62,64,79,96–104 ] …”

Section: Memristor Switching Mechanismsmentioning

confidence: 99%

Section: Ferroelectric Tunnel Junctionmentioning

confidence: 99%

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Essential Characteristics of Memristors for Neuromorphic Computing

Chen

Song

Wang

et al. 2022

Adv Elect Materials

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“…Memristive devices driven by magnetic tunneling are usually called magnetic tunnel junctions (MTJ). [93][94][95][96] The switching mechanism of MTJs is the spin-dependent tunneling effect. The tunneling conductance depends on the magnetization direction of the ferromagnetic layer.…”

Section: Realization Of Synaptic Plasticitymentioning

confidence: 99%

Memristor‐Based Intelligent Human‐Like Neural Computing

Wang

Song

Chen

et al. 2022

Adv Elect Materials

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In the field of a relatively dangerous working environment, NASA developed Valkyrie, a 44-degree-of-freedom, series elastic actuator-based robot. In addition, Valkyrie is designed to respond to disasters like nuclear disasters and advance human spaceflight in extraterrestrial planetary settings. [6] By implementing safety features and allowing remote intervention, Atkeson et al. enabled an Atlas humanoid robot to meet the standard in performing disaster response-related tasks involving physical contact with the environment. [7] Currently, to allow humanoid robots to feel and process the environmental information as human beings for perfectly implementing tasks, perceptual comprehension and computation efficiency are two key indexes. Nevertheless, it is challenging to achieve them. The former requires the installation of massive, diverse sensors in humanoids, which will inevitably slow down the processing speed under the current sensing-storage-process separated framework, i.e., von Neumann architecture. In other words, the current computer architecture builds up a barrier between sensing performance and computation efficiency. In this context, neuromorphic devices which can emulate the perceptual and computation functions of the biological nervous system have illustrated their potential to break the von Neumann barrier, attracting researchers' interests (Figure 1). Humanoid robots, intelligent machines resembling the human body in shape and functions, cannot only replace humans to complete services and dangerous tasks but also deepen the own understanding of the human body in the mimicking process. Nowadays, attaching a large number of sensors to obtain more sensory information and efficient computation is the development trend for humanoid robots. Nevertheless, due to the constraints of von Neumann-based structures, humanoid robots are facing multiple challenges, including tremendous energy consumption, latency bottlenecks, and the lack of bionic properties. Memristors, featured with high similarity to the biological elements, play an important role in mimicking the biological nervous system. The memristor-based nervous system allows humanoid robots to obtain high energy efficiency and bionic sensing properties, which are similar properties to the biological nervous system. Herein, this article first reviews the biological nervous system and memristor-based nervous system thoroughly, including the structures and also the functions. The applications of memristor-based nervous systems are introduced, the difficulties that need to be overcome are put forward, and future development prospects are also discussed. This review can hopefully provide an evolutionary perspective on humanoid robots and memristor-based nervous systems.

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“…Generally, a higher degree of crystallinity signifies more molecular chain arrangement, which requires a higher temperature to destroy, and the melting point is also increased. This also indirectly shows that adding Yb can improve the heat resistance of the material [ 19 , 20 , 21 , 22 , 23 , 24 ]. Moreover, a Co 60 Fe 20 B 20 thin film can be commonly used as a free or pinned layer in a magnetic tunneling junction (MTJ) structure, and it is necessary to improve TMR and thermal stability.…”

Section: Introductionmentioning

confidence: 99%

Effects of Annealing and Thickness of Co60Fe20Yb20 Nanofilms on Their Structure, Magnetic Properties, Electrical Efficiency, and Nanomechanical Characteristics

et al. 2022

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X-ray diffraction (XRD) analysis showed that metal oxide peaks appear at 2θ = 47.7°, 54.5°, and 56.3°, corresponding to Yb2O3 (440), Co2O3 (422), and Co2O3 (511). It was found that oxide formation plays an important role in magnetic, electrical, and surface energy. For magnetic and electrical measurements, the highest alternating current magnetic susceptibility (χac) and the lowest resistivity (×10−2 Ω·cm) were 0.213 and 0.42, respectively, and at 50 nm, it annealed at 300 °C due to weak oxide formation. For mechanical measurement, the highest value of hardness was 15.93 GPa at 200 °C in a 50 nm thick film. When the thickness increased from 10 to 50 nm, the hardness and Young’s modulus of the Co60Fe20Yb20 film also showed a saturation trend. After annealing at 300 °C, Co60Fe20Yb20 films of 40 nm thickness showed the highest surface energy. Higher surface energy indicated stronger adhesion, allowing for the formation of multilayer thin films. The optimal condition was found to be 50 nm with annealing at 300 °C due to high χac, strong adhesion, high nano-mechanical properties, and low resistivity.

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Dilute magnetic semiconductor electrode based all semiconductor magnetic tunnel junction for high-temperature applications

Cited by 5 publications

References 31 publications

Essential Characteristics of Memristors for Neuromorphic Computing

Essential Characteristics of Memristors for Neuromorphic Computing

Memristor‐Based Intelligent Human‐Like Neural Computing

Effects of Annealing and Thickness of Co60Fe20Yb20 Nanofilms on Their Structure, Magnetic Properties, Electrical Efficiency, and Nanomechanical Characteristics

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