当前位置:首页 >期刊论文 >《最新论文》>正文

科学家研制出非线性忆阻计算光谱仪

 2025/1/17 14:13:25 《最新论文》 作者:科学网 小柯机器人 我有话说(0人评论) 字体大小:+

近日,365体育投注:上海技术物理研究所的李冠海及其研究团队取得一项新进展。经过不懈努力,他们研制出非线性忆阻计算光谱仪。相关研究成果已于2025年1月14日在国际知名学术期刊《光:科学与应用》上发表。

该研究团队引入一种基于WSe2同质结的非线性光子忆阻器驱动的计算光谱仪。该方法通过钯(Pd)离子迁移驱动的动态能带调制,克服了传统限制,如费米能级可调性受限、持续存在的暗电流以及光响应维度有限等问题。第一性原理计算、数值模拟和实验验证均充分支持了Pd离子迁移的关键作用,证明了其在提升器件性能方面的有效性。

此外,研究人员将这种动态调制与专为处理忆阻器固有非线性光响应设计的非线性神经网络相结合。这种结合使该研究的光谱仪在630~640nm范围内实现了0.18nm的卓越峰值波长准确度和2nm的光谱分辨率。这一进展标志着在制造紧凑型、高效光谱仪器方面取得了重大突破,并为跨多种材料系统的应用提供了通用平台。

据悉,在光谱学领域,小型化工作往往面临重大挑战,尤其是在实现高光谱分辨率和精确构造方面。

附:英文原文

Title: Nonlinear memristive computational spectrometer

Author: Li, Xin, Wang, Jie, Yu, Feilong, Chen, Jin, Chen, Xiaoshuang, Lu, Wei, Li, Guanhai

Issue&Volume: 2025-01-14

Abstract: In the domain of spectroscopy, miniaturization efforts often face significant challenges, particularly in achieving high spectral resolution and precise construction. Here, we introduce a computational spectrometer powered by a nonlinear photonic memristor with a WSe2 homojunction. This approach overcomes traditional limitations, such as constrained Fermi level tunability, persistent dark current, and limited photoresponse dimensionality through dynamic energy band modulation driven by palladium (Pd) ion migration. The critical role of Pd ion migration is thoroughly supported by first-principles calculations, numerical simulations, and experimental verification, demonstrating its effectiveness in enhancing device performance. Additionally, we integrate this dynamic modulation with a specialized nonlinear neural network tailored to address the memristor’s inherent nonlinear photoresponse. This combination enables our spectrometer to achieve an exceptional peak wavelength accuracy of 0.18nm and a spectral resolution of 2nm within the 630–640nm range. This development marks a significant advancement in the creation of compact, high-efficiency spectroscopic instruments and offers a versatile platform for applications across diverse material systems.

DOI: 10.1038/s41377-024-01703-y

Source: https://www.nature.com/articles/s41377-024-01703-y

来源:科学网  小柯机器人 

版权声明:本文转载仅仅是出于传播信息的需要,并不意味着代表本网站观点或证实其内容的真实性;如其他媒体、网站或个人从本网站转载使用,须保留本网站注明的“来源”,并自负版权等法律责任;作者如果不希望被转载或者联系转载稿费等事宜,请与我们接洽。