摘要
论文以无序色散成像光谱仪的制备与性能测试为例,研究了如何将新型的上、下转换发光材料和成像光谱仪器件相结合实现宽波段测量范围的超光谱成像,并将其应用于材料物理专业本科生"光电子器件与工艺"课程的实践教学中去.该实践教学包括前置器件、色散器件、转换器件、探测器件等部件的设计与制备,以及光谱复原数学优化算法的学习和大型实验仪器的使用.该实验教学设计,改变了传统材料专业学生对光电子技术不够了解的状况,扩大了材料物理专业学生在光电子学领域的知识面,提高了综合素养,促进了多元化就业.
Abstract
[Objective]We explore the combination of new upconversion or downconversion light-emitting materials with imaging spectrometer devices to achieve hyperspectral imaging in a broad spectral range.We use the preparation and performance test of a disordered dispersion imaging spectrometer as an example.This approach has been applied to the practical teaching of"Optoelectronic devices and manufacture processes"for undergraduates majoring in material physics.Imaging spectrometers are known to simultaneously obtain two-dimensional(2D)imaging and spectra from each pixel of the 2D imaging.Nowadays,hyperspectral imaging technology is widely used in various fields such as military reconnaissance,atmospheric exploration,space remote sensing,general survey of earth resources,environmental monitoring,agriculture,and marine remote sensing.However,conventional imaging spectrometers are large and expensive.Furthermore,they normally cannot realize static measurement and high-resolution spectral imaging in a wide spectral range,which limits their applications,such as hyperspectral imaging and broadband biochemical detection.To overcome these limitations,we propose a novel disordered dispersion imaging spectrometer.This instrument offers significant advantages over existing instruments based on Fourier transform and grating dispersion,including size reduction,high space resolution,high spectral resolution,broad spectral range,low cost,and high reliability.The main aim of this paper is to design the key components of the disordered dispersion imaging spectrometer:a dispersion component,a conversion component,and a detection component.[Methods]The dispersion component uses disordered dispersive structures such as random nanoholes or nanoparticles to reduce the fabrication complexity and system size.The conversion component employs upconversion and downconversion materials to broaden the operation spectral range of the system to infrared(IR)and ultraviolet(UV)bands.The detection component utilizes imaging chips,such as CCD or CMOS,which have a large number of pixels.This contributes to increasing both the spatial and spectral resolution.Unlike previous spectrum reconstruction technologies,we use nonlinear optimization algorithms for ill-posed problems to solve matrix equations.The solution of each matrix equation corresponds to the spectrum of each pixel of the pending image being analyzed.In addition,we propose two data acquisition methods:the serial measurement method and the multithread measurement method.These are designed to achieve hyperspectral imaging and real-time static spectral imaging,respectively.[Results]Following the implementation of our demonstration experiment,we developed a novel,high-performance imaging spectrometer,which provided students with practical experience in designing and preparing various devices,such as incident,dispersion,conversion,and detection devices.Consequently,the students are encouraged to use the mathematical optimization algorithm for spectral reconstruction and the use of large experimental instruments.[Conclusions]The teaching reform is particularly beneficial for students with a background in traditional material majors as it exposes them to optoelectronic technology.The outcome is a broadening of their optoelectronic knowledge and an improvement in their comprehensive capabilities.This approach will promote diversified employment opportunities for students majoring in material physics.Furthermore,the capabilities of existing imaging spectrometers have been further improved,which is a significant step forward for the spectrum industry in our country.This advancement brings us closer to achieving international standards.
基金项目
国家自然科学基金面上项目(62074082)
国家自然科学基金面上项目(21973047)
南京邮电大学教学改革重点项目(JG03022JX04)
南京邮电大学教学改革一般项目(JG03022JX23)
南京邮电大学教学改革一般项目(JG03022JX70)
维普
万方数据