Emerging phase change memory devices using non-oxide semiconducting glasses

Agarwal, Surbhi; Lohia, Pooja; Dwivedi, D. K.

doi:10.1016/j.jnoncrysol.2022.121874

Cited by 20 publications

(7 citation statements)

References 172 publications

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“…Experiments have demonstrated that the PCM reveals an ultralarge array size and sophisticated fabrication innovations, [61][62][63] which can corroborate more complicated workloads and algorithms. The conductive drift phenomena in the PCM is also determined physically through structural relaxation after the writing process and is inherent in character, [64,65] which is general and resilient and does not require specialized programming conditions/material adoptions. Finally, the same PCM element can be utilized for both storage and processing, further facilitating hardware cost reduction.…”

Section: Sf R2 Evaluationmentioning

confidence: 99%

A Phase‐Change Memristive Reinforcement Learning for Rapidly Outperforming Champion Street‐Fighter Players

Go,

Jiang,

Loke

2023

Advanced Intelligent Systems

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The interactions with humans, and simultaneously, making of real‐time decisions in physical systems, are involved in many applications of artificial intelligence. An example of these conditions is maneuver sports. Movement‐type simulations, viz., the esports game Street Fighter (SF), recapitulate the complex multicharacter interactions and, concurrently, generate the millisecond‐level control challenges of human athletes. Herein, the physical and mental signatures of the SF agent (it is called SF R2) are controlled by utilizing a previously unreported model‐free, natural, deep reinforcement learning algorithm “Decay‐based Phase‐change memristive character‐type Proximal Policy Optimization” (DP‐PPO) through an assemblage of hybrid case‐type training processes; and an integrated training configuration for time‐trial evaluations, as well as competitions with a world's best SF player, is developed. A short length of time utilized by the SF R2 to defeat the opponent and, simultaneously, maintaining a good health level is achieved, as well as excellent handling of imperfect information settings. Training studies reveal a moderate maneuver etiquette in the SF R2, along with rapid, effective head‐to‐head competitions with one of the world's best SF player. This paves the way for achieving a broadly applicable training scheme, capable of quickly controlling complicated‐movement systems in fields where agents should observe unspecified human norms.

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Section: Sf R2 Evaluationmentioning

confidence: 99%

A Phase‐Change Memristive Reinforcement Learning for Rapidly Outperforming Champion Street‐Fighter Players

Go,

Jiang,

Loke

2023

Advanced Intelligent Systems

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“…EPCMC cells have a similar micro architecture and access granularity to DRAM chips, which makes them suitable for main memory applications [3]. Studies conducted in the recent past have analyzed the behavior of EPCM cells and estimated the voltage margin at partial lines [4]. EPCM arrays are limited to two levels/cells due to drift obstacles, and they have a write latency that is four times greater than DRAM arrays.…”

Section: Introductionmentioning

confidence: 99%

OML-PCM: optical multi-level phase change memory architecture for embedded computing systems

Mohseni,

Novin

2023

Eng. Res. Express

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Unlike Dynamic Random Access Memory (DRAM), Phase Change Memory (PCM) offers higher density, longer data retention, and improved scalability because of its non-volatility and low leakage power. However, Electrically-Addressable PCM (EPCM) has a higher dynamic power and long latency than DRAM. To address these issues, scientists have developed Optically-Addressable PCM (OPCM), which uses 5-level cells instead of 2-level cells in EPCM. A silicon photonic link allows optical signals to reach OPCM cells at a high speed. Hence, OPCM can achieve a higher density while maintaining better performance at multi-level cells and consuming less power per access. However, OPCM is not suitable for general use since the photonic links do not provide an electrical interface to the processor. The aim of this paper is to present a hybrid OPCM architecture based on the use of novel multi-bank clusters with distinctive properties. Electrical-Optical-Electrical conversion (EOE) allows OPCM cells to be randomly accessed by using DRAM-like circuitry. The proposed hybrid design with multi-core processing and OPCM achieves a 2.13x speedup over previous approaches while consuming less Central Processing Unit (CPU) power. It is important to note that the proposed design offers 97 units fewer power-consistent bits than EPCM. In addition, the proposed architecture provides comparable performance and power to DDR4, as well as improved bandwidth density, space efficiency, and versatility. The Gem5 simulator was used to evaluate the design. Based on the outcomes of the analysis, the proposed architecture offers 2.08x and 2.14x better evaluations and density performance than EPCM. Furthermore, the execution time has been reduced by 2.13x, the analysis time by 1.23x, and the composition time by 4.60%.

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“…Incorporation of elements such as Ge, Te, Sb, In, Pb, Bi, and As leads to remarkable changes in the properties and a change in the structure of compounds, in addition to a slow aging effect, high crystallization temperature, and improved photosensitivity. 7 The addition of tellurium to Se reduces the limitations and enhances the properties of the resulting compound. Se-Te alloys have various scientific and technological importance.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of dielectric relaxation and AC conductivity of Te_(1−x)(GeSe_0.5)Sc_x (0 ≤ x ≤ 0.15) chalcogenide glasses by a rare-earth dopant

Agarwal,

Lohia,

Dwivedi

2023

New J. Chem.

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Emerging phase change memory devices using non-oxide semiconducting glasses

Cited by 20 publications

References 172 publications

A Phase‐Change Memristive Reinforcement Learning for Rapidly Outperforming Champion Street‐Fighter Players

A Phase‐Change Memristive Reinforcement Learning for Rapidly Outperforming Champion Street‐Fighter Players

OML-PCM: optical multi-level phase change memory architecture for embedded computing systems

Enhancement of dielectric relaxation and AC conductivity of Te_(1−x)(GeSe_0.5)Sc_x (0 ≤ x ≤ 0.15) chalcogenide glasses by a rare-earth dopant

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Emerging phase change memory devices using non-oxide semiconducting glasses

Cited by 20 publications

References 172 publications

A Phase‐Change Memristive Reinforcement Learning for Rapidly Outperforming Champion Street‐Fighter Players

A Phase‐Change Memristive Reinforcement Learning for Rapidly Outperforming Champion Street‐Fighter Players

OML-PCM: optical multi-level phase change memory architecture for embedded computing systems

Enhancement of dielectric relaxation and AC conductivity of Te(1−x)(GeSe0.5)Scx (0 ≤ x ≤ 0.15) chalcogenide glasses by a rare-earth dopant

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Enhancement of dielectric relaxation and AC conductivity of Te_(1−x)(GeSe_0.5)Sc_x (0 ≤ x ≤ 0.15) chalcogenide glasses by a rare-earth dopant