Artificial Intelligence and Advanced Materials DOI Creative Commons
Cefe López

Advanced Materials, Journal Year: 2022, Volume and Issue: 35(23)

Published: Dec. 23, 2022

Abstract Artificial intelligence (AI) is gaining strength, and materials science can both contribute to profit from it. In a simultaneous progress race, new materials, systems, processes be devised optimized thanks machine learning (ML) techniques, such turned into innovative computing platforms. Future scientists will understanding how ML boost the conception of advanced materials. This review covers aspects computation fundamentals directions taken repercussions produced by account for origins, procedures, applications AI. its methods are reviewed provide basic knowledge implementation potential. The systems used implement AI with electric charges finding serious competition other information‐carrying processing agents. impact these techniques have on inception so deep that paradigm developing where implicit being mined conceive functions instead found How far this trend carried hard fathom, as exemplified power discover unheard or physical laws buried in data.

Language: Английский

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems DOI
Min‐Kyu Song, Ji‐Hoon Kang, Xinyuan Zhang

et al.

ACS Nano, Journal Year: 2023, Volume and Issue: 17(13), P. 11994 - 12039

Published: June 29, 2023

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated memristors 2008, memristive devices have garnered significant attention due their biomimetic memory properties, promise significantly improve power consumption computing applications. Here, we provide comprehensive overview of recent advances including devices, theory, algorithms, architectures, and systems. In addition, discuss research directions for various applications hardware accelerators artificial intelligence, in-sensor computing, probabilistic computing. Finally, forward-looking perspective on the future outlining challenges opportunities further innovation this field. By providing an up-to-date state-of-the-art review aims inform inspire

Language: Английский

Citations

134
Integrated optical memristors DOI
Nathan Youngblood, Carlos Rı́os, Wolfram H. P. Pernice

et al.

Nature Photonics, Journal Year: 2023, Volume and Issue: 17(7), P. 561 - 572

Published: May 29, 2023

Language: Английский

Citations

85
A ferroelectric multilevel non-volatile photonic phase shifter DOI
Jacqueline Geler-Kremer, Felix Eltes, Pascal Stark

et al.

Nature Photonics, Journal Year: 2022, Volume and Issue: 16(7), P. 491 - 497

Published: May 30, 2022

Language: Английский

Citations

76
In-memory photonic dot-product engine with electrically programmable weight banks DOI Creative Commons
Wen Zhou, Bowei Dong, Nikolaos Farmakidis

et al.

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: May 20, 2023

Abstract Electronically reprogrammable photonic circuits based on phase-change chalcogenides present an avenue to resolve the von-Neumann bottleneck; however, implementation of such hybrid photonic–electronic processing has not achieved computational success. Here, we achieve this milestone by demonstrating in-memory dot-product engine, one that decouples electronic programming materials (PCMs) and computation. Specifically, develop non-volatile electronically PCM memory cells with a record-high 4-bit weight encoding, lowest energy consumption per unit modulation depth (1.7 nJ/dB) for Erase operation (crystallization), high switching contrast (158.5%) using non-resonant silicon-on-insulator waveguide microheater devices. This enables us perform parallel multiplications image superior contrast-to-noise ratio (≥87.36) leads enhanced computing accuracy (standard deviation σ ≤ 0.007). An system is developed in hardware convolutional recognizing images from MNIST database inferencing accuracies 86% 87%.

Language: Английский

Citations

76
Non-volatile electrically programmable integrated photonics with a 5-bit operation DOI Creative Commons
Rui Chen, Zhuoran Fang, Christopher J. Perez

et al.

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: June 12, 2023

Scalable programmable photonic integrated circuits (PICs) can potentially transform the current state of classical and quantum optical information processing. However, traditional means programming, including thermo-optic, free carrier dispersion, Pockels effect result in either large device footprints or high static energy consumptions, significantly limiting their scalability. While chalcogenide-based non-volatile phase-change materials (PCMs) could mitigate these problems thanks to strong index modulation zero power consumption, they often suffer from absorptive loss, low cyclability, lack multilevel operation. Here, we report a wide-bandgap PCM antimony sulfide (Sb2S3)-clad silicon platform simultaneously achieving 5-bit We switch Sb2S3 via an on-chip PIN diode heater demonstrate components with insertion loss (<1.0 dB), extinction ratio (>10 endurance (>1,600 switching events). Remarkably, find that be programmed into fine intermediate states by applying identical thermally isolated pulses, providing unique approach controllable Through dynamic pulse control, achieve on-demand accurate (32 levels) operations, rendering 0.50 +- 0.16 dB contrast per step. Using this behavior, further trim random phase error balanced Mach-Zehnder interferometer. Our work opens attractive pathway toward large-scale PICs low-loss multi-bit operations.

Language: Английский

Citations

69
Wavelength-shift-free racetrack resonator hybrided with phase change material for photonic in-memory computing DOI Creative Commons

honghui zhu,

Yegang Lü,

linying cai

et al.

Optics Express, Journal Year: 2023, Volume and Issue: 31(12), P. 18840 - 18840

Published: May 2, 2023

The photonic in-memory computing architecture based on phase change materials (PCMs) is increasingly attracting widespread attention due to its high computational efficiency and low power consumption. However, PCM-based microring resonator devices face challenges in terms of resonant wavelength shift (RWS) for large-scale network. Here, we propose a PCM-slot-based 1 × 2 racetrack with free computing. low-loss PCMs such as Sb2Se3 Sb2S3 are utilized fill the waveguide slot insertion (IL) extinction ratio (ER). Sb2Se3-slot-based has an IL 1.3 (0.1) dB ER 35.5 (8.6) at drop (through) port. corresponding 0.84 (0.27) 18.6 (10.11) obtained Sb2S3-slot-based device. optical transmittance two more than 80%. No resonance can be achieved upon among multi-level states. Moreover, device exhibits degree fabrication tolerance. proposed demonstrates ultra-low RWS, transmittance-tuning range, IL, which provides new scheme realizing energy-efficient

Language: Английский

Citations

51
Roadmap for phase change materials in photonics and beyond DOI Creative Commons

Patinharekandy Prabhathan,

Kandammathe Valiyaveedu Sreekanth, Jinghua Teng

et al.

iScience, Journal Year: 2023, Volume and Issue: 26(10), P. 107946 - 107946

Published: Sept. 22, 2023

Phase Change Materials (PCMs) have demonstrated tremendous potential as a platform for achieving diverse functionalities in active and reconfigurable micro-nanophotonic devices across the electromagnetic spectrum, ranging from terahertz to visible frequencies. This comprehensive roadmap reviews material device aspects of PCMs, their applications spectrum. It discusses various configurations optimization techniques, including deep learning-based metasurface design. The integration PCMs with Photonic Integrated Circuits advanced electric-driven are explored. hold great promise multifunctional development, non-volatile memory, optical data storage, photonics, energy harvesting, biomedical technology, neuromorphic computing, thermal management, flexible electronics.

Language: Английский

Citations

46
Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration DOI
Minyi Xu, Xinrui Chen, Yehao Guo

et al.

Advanced Materials, Journal Year: 2023, Volume and Issue: 35(51)

Published: June 7, 2023

Abstract Neuromorphic computing has been attracting ever‐increasing attention due to superior energy efficiency, with great promise promote the next wave of artificial general intelligence in post‐Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, data‐intensive that domain. Reconfigurable neuromorphic computing, an on‐demand paradigm inspired by inherent programmability brain, can maximally reallocate finite resources perform proliferation reproducibly brain‐inspired functions, highlighting a disruptive framework bridging gap between different primitives. Although relevant research flourished diverse materials devices novel mechanisms architectures, precise overview remains blank urgently desirable. Herein, recent strides along this pursuit are systematically reviewed from material, device, integration perspectives. At material device level, one comprehensively conclude dominant reconfigurability, categorized into ion migration, carrier phase transition, spintronics, photonics. Integration‐level developments reconfigurable also exhibited. Finally, perspective on future challenges is discussed, definitely expanding its horizon scientific communities.

Language: Английский

Citations

45
Myths and truths about optical phase change materials: A perspective DOI Creative Commons
Yifei Zhang, Carlos Rı́os, Mikhail Y. Shalaginov

et al.

Applied Physics Letters, Journal Year: 2021, Volume and Issue: 118(21)

Published: May 24, 2021

Uniquely furnishing giant and nonvolatile modulation of optical properties chalcogenide phase change materials (PCMs) have emerged as a promising material to transform integrated photonics free-space optics alike. The surge interest in these warrants thorough understanding their characteristics specifically the context photonic applications. This article seeks clarify some commonly held misconceptions about PCMs offer perspective on new research frontiers field.

Language: Английский

Citations

99
Modeling Electrical Switching of Nonvolatile Phase-Change Integrated Nanophotonic Structures with Graphene Heaters DOI
Jiajiu Zheng, Shifeng Zhu, Peipeng Xu

et al.

ACS Applied Materials & Interfaces, Journal Year: 2020, Volume and Issue: 12(19), P. 21827 - 21836

Published: April 16, 2020

Progress in integrated nanophotonics has enabled large-scale programmable photonic circuits (PICs) for general-purpose electronic-photonic systems on a chip. Relying the weak, volatile thermo-optic or electro-optic effects, such usually exhibit limited reconfigurability along with high energy consumption and large footprints. These challenges can be addressed by resorting to chalcogenide phase-change materials (PCMs) as Ge2Sb2Te5 (GST) that provide substantial optical contrast self-holding fashion upon phase transitions. However, current PCM-based applications are single devices simple PICs due poor scalability of electrical self-heating actuation approaches. Thermal-conduction heating via external heaters, instead, allows integration large-area switching, but fast energy-efficient control is yet show. Here, we model switching GST-clad nanophotonic structures graphene heaters based GST-on-silicon platform. Thanks ultra-low heat capacity in-plane thermal conductivity graphene, proposed speed ~80 MHz efficiency 19.2 aJ/nm^3 (6.6 aJ/nm^3) crystallization (amorphization) while achieving complete transitions ensure strong attenuation (~6.46 dB/micron) (~0.28 dB/micron at 1550 nm) modulation. Compared indium tin oxide silicon p-i-n display two orders magnitude higher figure merits overall performance. Our work facilitates analysis understanding thermal-conduction heating-enabled supports development future systems.

Language: Английский

Citations

97