Enhancing Focusing and Defocusing Capabilities with a Dynamically Reconfigurable Metalens Utilizing Sb2Se3 Phase-Change Material

Shen, Chen; Ye, Jiachi; Peserico, Nicola; Gui, Yaliang; Dong, Chaobo; Kang, Haoyan; Nouri, Behrouz Movahhed; Wang, Hao; Heidari, Elham; Sorger, Volker J.; Dalir, Hamed

doi:10.3390/nano13142106

Cited by 15 publications

(5 citation statements)

References 49 publications

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“…3 provides an experimental outcome showing the noteworthy disparity in optical power between the two primary states of a PCM cell: the amorphous and polycrystalline states. [89][90][91][92] This marked disparity in optical power levels not only underscores the binary operational mode of PCM, enabling straightforward digital logic representation, but also illuminates the vast potential for implementing sophisticated multi-level storage solutions within the same physical memory cell. The pronounced optical power gap depicted in Fig.…”

Section: Phase Change Memorymentioning

confidence: 99%

Optical memory in IoT devices: an energy-efficient approach using phase change materials

Jahannia,

Ye,

Altaleb

et al. 2024

AI and Optical Data Sciences V

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As Internet-of-Things (IoT) devices continue to grow rapidly in number, developing energy-efficient memory solutions has become critically important. This paper introduces an innovative Phase Change Memory (PCM) architecture that can significantly reduce memory energy consumption in IoT devices. After highlighting the energy-inefficiency of current memory designs, we explore the possibilities of leveraging PCM. We demonstrate that the benefits of exploiting PCM are dependent on the working frequency of the CPU and show how PCM can surpass devices with SRAM and DRAM. As a replacement candidate for FLASH, PCM can also be utilized instead of SRAM and DRAM. We also demonstrate that, based on the application, we can also save more energy. Ongoing work focuses on deployment of this application dependency and enhancing energy efficiency of devices using PCM. This represents a major advance towards realizing widespread integration of photonics and electronics in IoT hardware.

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Section: Phase Change Memorymentioning

confidence: 99%

Optical memory in IoT devices: an energy-efficient approach using phase change materials

Jahannia,

Ye,

Altaleb

et al. 2024

AI and Optical Data Sciences V

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“…Their versatility and efficiency in managing thermal and optical properties continue to drive innovation in diverse fields, offering solutions for energy conservation and data management. [ 96 ]…”

Section: Reconfigurable Mlsmentioning

confidence: 99%

A Review on Reconfigurable Metalenses Revolutionizing Flat Optics

Khonina,

Butt,

Kazanskiy

2023

Advanced Optical Materials

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Metalenses (MLs), representing a groundbreaking paradigm shift within the realm of optics, have ushered in a transformative era in the ability to manipulate and harness the power of light. Diverging from the conventional approach reliant on curved surfaces to bend light, MLs harness intricate arrays of minuscule nanostructures to exert precise control over the phase and amplitude of incident light waves. This revolutionary technology bestows a plethora of advantages, including the creation of ultra‐compact optical systems, the enhancement of focusing capabilities to unprecedented levels, and the capability to rectify optical aberrations in a significantly thinner and more lightweight form factor. MLs have discovered a multitude of applications spanning diverse fields, from imaging and photography to augmenting reality and refining medical devices. They stand as a beacon of hope for reshaping the landscape of optical systems, courtesy of their remarkable adaptability and exceptional performance. As the evolution of MLs forges ahead, they hold immense potential to transcend the boundaries of what can be accomplished in the domain of light‐based technologies. This review presents the recent advances in reconfigurable MLs and their applicability to imaging systems.

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“…As a result, there is a reduced tolerance for alignment errors in systems utilizing OAM beams, necessitating more precise and sensitive alignment mechanisms to maintain effective communication. [42][43][44] Machine learning has become a pivotal technology in optics, [45][46][47][48][49][50] photonics, [51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68] and computer science. [69][70][71][72][73][74][75][76][77] This is evidenced by its applications in learning-based imaging and focusing through scattering mediums, as well as in computer-generated holography, where machine learning techniques have been effectively utilized.…”

Section: Introductionmentioning

confidence: 99%

OAM modes classification and demultiplexing via Fourier optical neural network

Ye,

Jahannia,

Kang

et al. 2024

Complex Light and Optical Forces XVIII

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Here, we present a free-space optical communication system, adept at managing alignment deviations and the challenges posed by the atmospheric turbulence in the transmission of spatially structured laser beams, particularly those carrying orbital angular momentum (OAM). The core of our system is tilizing a hybrid convolutional neural network (HCNN). This network is uniquely characterized by the first layer designed as a Fourier optics convolutional layer, implemented optically through a 4f system. This system is dynamically controlled by high-resolution, kilohertz-fast reprogrammable digital micromirror device (DMD). The primary innovation is employing this optical-filtering-based CNN for the training and recognition of multiplexed OAM beams with their unique intensity patterns. The experiments were conducted under conditions simulating atmospheric turbulence based on modified von Kármán model. The system demonstrated promising accuracy, achieving 78.25% under weak turbulence condition and 68.43% in scenarios of strong turbulence.

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Enhancing Focusing and Defocusing Capabilities with a Dynamically Reconfigurable Metalens Utilizing Sb2Se3 Phase-Change Material

Cited by 15 publications

References 49 publications

Optical memory in IoT devices: an energy-efficient approach using phase change materials

Optical memory in IoT devices: an energy-efficient approach using phase change materials

A Review on Reconfigurable Metalenses Revolutionizing Flat Optics

OAM modes classification and demultiplexing via Fourier optical neural network

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