This PDF file contains the front matter associated with SPIE Proceedings Volume 10210, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

We describe a versatile mid-infrared (Mid-IR) spectroscopy system developed to measure the concentration of a wide range of gases with an ultra-low sample size. The system combines a rapidly-swept external cavity quantum cascade laser (ECQCL) with a hollow fiber gas cell. The ECQCL has sufficient spectral resolution and reproducibility to measure gases with narrow features (e.g., water, methane, ammonia, etc.), and also the spectral tuning range needed to measure volatile organic compounds (VOCs), (e.g., aldehydes, ketones, hydrocarbons), sulfur compounds, chlorine compounds, etc. The hollow fiber is a capillary tube having an internal reflective coating optimized for transmitting the Mid-IR laser beam to a detector. Sample gas introduced into the fiber (e.g., internal volume = 0.6 ml) interacts strongly with the laser beam, and despite relatively modest path lengths (e.g., L ~ 3 m), the requisite quantity of sample needed for sensitive measurements can be significantly less than what is required using conventional IR laser spectroscopy systems. Example measurements are presented including quantification of VOCs relevant for human breath analysis with a sensitivity of ~2 picomoles at a 1 Hz data rate.

Due to their high brightness, infrared (IR) lasers (such as tunable quantum cascade lasers, QCLLs) are very attractive illumination sources in both stand-off spectroscopy and micro-spectroscopy. In fact, they are the enabling device for trace-level spectroscopy. However, due to their high coherence as laser beams, QCLLs can cause speckle, especially when illuminating a rough surface. This is highly detrimental to the signal-to-noise ratio (SNR) of thee collected spectra and can easily negate the gains from using aa high brightness source. In most cases, speckle reduction is performed at the expense of optical power. In this paper, we examine several speckle reduction approaches and evaluate them for their ability to reduce speckle contrast while at the same time preserving aa high optical throughput. We analyze multi-mode fibers, integrating spheres, and stationary and moving diffusers for their speckle reduction potential. Speckle-contrast is measured directly by acquiring beam profiles of the illumination beam or, indirectly, by observing speckle formation from illuminating a rough surface (e.g. Infragold® coated surface) with an IR micro-bolometer camera. We also report on a novel speckle-reducing device with increased optical throughput. We characterize speckle contrast reduction from spatial, temporal and wavelength averaging for both CWW and pulsed QCLs. Examples of effect of speckle-reduction on hyperspectral images in both standoff and microscopy configurations are given.

This paper reports a novel quantitative gas plume imaging tool, based on active near-infrared Backscatter Tunable Diode Laser Absorption Spectroscopy (b-TDLAS) technology, designed for upstream natural gas leak applications. The new tool integrates low-cost laser sensors with video cameras to create a highly sensitive gas plume imager that also quantifies emission rate, all in a lightweight handheld ergonomic package. It is intended to serve as a lower-cost, higherperformance, enhanced functionality replacement for traditional passive non-quantitative mid-infrared Optical Gas Imagers (OGI) which are utilized by industry to comply with natural gas infrastructure Leak Detection and Repair (LDAR) requirements. It addresses the need for reliable, robust, low-cost sensors to detect and image methane leaks, and to quantify leak emission rates, focusing on inspections of upstream oil and gas operations, such as well pads, compressors, and gas plants. It provides: 1) Colorized quantified images of path-integrated methane concentration. The images depict methane plumes (otherwise invisible to the eye) actively interrogated by the laser beam overlaid on a visible camera image of the background. The detection sensitivity exceeds passive OGI, thus simplifying the manual task of leak detection and location; and 2) Data and algorithms for using the quantitative information gathered by the active detection technique to deduce plume flux (i.e. methane emission rate). This key capability will enable operators to prioritize leak repairs and thereby minimize the value of lost product, as well as to quantify and minimize greenhouse gas emissions, using a tool that meets EPA LDAR imaging equipment requirements.

An advanced commercial Mid-InfraRed Isotope Ratio (IR²) analyzer was developed in Arrow Grand Technologies based on hollow waveguide (HWG) as the sample tube. The stable carbon isotope ratio, i.e. δ¹³C, was obtained by measuring the selected CO² absorption peaks in the MIR. Combined with a GC and a combustor, it has been successfully employed to measure compound specific δ¹³C isotope ratios in the field. By using both the 1- pass HWG and 5-path HWG, we are able to measure δ¹³C isotope ratio at a broad CO₂ concentration of 300 ppm-37,500 ppm. Here, we demonstrate its applications in environmental studies. The δ¹³C isotope ratio and concentration of CO₂ exhaled by soil samples was measured in real time with the isotope analyzer. The concentration was found to change with the time. We also convert the Dissolved Inorganic Carbon (DIC) into CO₂, and then measure the δ¹³C isotope ratio with an accuracy of better than 0.3 ‰ (1 σ) with a 6 min test time and 1 ml sample usage. Tap water, NaHCO3 solvent, coca, and even beer were tested. Lastly, the 13C isotope ratio of CO2 exhaled by human beings was obtained <10 seconds after simply blowing the exhaled CO2 into a tube with an accuracy of 0.5‰ (1 σ) without sample preconditioning. In summary, a commercial HWG isotope analyzer was demonstrated to be able to perform environmental and health studies with a high accuracy (~0.3 ‰/Hz^1/2 1 σ), fast sampling rate (up to 10 Hz), low sample consumption (~1 ml), and broad CO2 concentration range (300 ppm-37,500 ppm).

There are many applications requiring chemical analysis in the field and analytical results in (near) real-time. For example, when accidental spills occur. In others, collecting samples in the field followed by analysis in a lab increases costs and introduces time-delays. In such cases, “bring part of the lab to the sample” would be ideal. Toward this ideal (and to further reduce size and weight), we developed a relatively inexpensive, battery-operated, wireless data acquisition hardware system around an Arduino nano micro-controller and a 16-bit ADC (Analog-to- Digital Converter) with a max sampling rate of 860 samples/s. The hardware communicates the acquired data using low-power Bluetooth. Software for data acquisition and data display was written in Python. Potential ways of making the hardware-software approach described here a part of the Internet-of-Things (IoT) are presented.

Fourier transform spectroscopy has become a standard method for spectral analysis of infrared light. With this method, an interferogram is created by two beam interference which is subsequently Fourier-transformed. Most Fourier transform spectrometers used today provide the interferogram in the temporal domain. In contrast, static Fourier transform spectrometers generate interferograms in the spatial domain.

One example of this type of spectrometer is the static single-mirror Fourier transform spectrometer which offers a high etendue in combination with a simple, miniaturized optics design. As no moving parts are required, it also features a high vibration resistance and high measurement rates. However, it is susceptible to temperature variations. In this paper, we therefore discuss the main sources for temperature-induced errors in static single-mirror Fourier transform spectrometers: changes in the refractive index of the optical components used, variations of the detector sensitivity, and thermal expansion of the housing. As these errors manifest themselves in temperature-dependent wavenumber shifts and intensity shifts, they prevent static single-mirror Fourier transform spectrometers from delivering long-term stable spectra.

To eliminate these shifts, we additionally present a work concept for the thermal stabilization of the spectrometer. With this stabilization, static single-mirror Fourier transform spectrometers are made suitable for infrared process spectroscopy under harsh thermal environmental conditions. As the static single-mirror Fourier transform spectrometer uses the so-called source-doubling principle, many of the mentioned findings are transferable to other designs of static Fourier transform spectrometers based on the same principle.

Portable and handheld spectrometers are being developed and commercialized in the late few years leveraging the rapidly-progressing technology and triggering new markets in the field of on-site spectroscopic analysis. Although handheld devices were commercialized for the near-infrared spectroscopy (NIRS), their size and cost stand as an obstacle against the deployment of the spectrometer as spectral sensing components needed for the smart phone industry and the IoT applications. In this work we report a chip-sized microelectromechanical system (MEMS)-based FTIR spectrometer. The core optical engine of the solution is built using a passive-alignment integration technique for a selfaligned MEMS chip; self-aligned microoptics and a single detector in a tiny package sized about 1 cm³. The MEMS chip is a monolithic, high-throughput scanning Michelson interferometer fabricated using deep reactive ion etching technology of silicon-on-insulator substrate. The micro-optical part is used for conditioning the input/output light to/from the MEMS and for further light direction to the detector. Thanks to the all-reflective design of the conditioning microoptics, the performance is free of chromatic aberration. Complemented by the excellent transmission properties of the silicon in the infrared region, the integrated solution allows very wide spectral range of operation. The reported sensor’s spectral resolution is about 33 cm^-1 and working in the range of 1270 nm to 2700 nm; upper limited by the extended InGaAs detector. The presented solution provides a low cost, low power, tiny size, wide wavelength range NIR spectral sensor that can be manufactured with extremely high volumes. All these features promise the compatibility of this technology with the forthcoming demand of smart portable and IoT devices.

Time multiplexed spectral images of burning aluminum particles from two experiments using a hyperspectral imaging system (HIS) coupled to a high speed video (HSV) camera were investigated. The first experiment looks at ignited aluminum particles generated by a welding torch that were continuously funneled into the imaging plane of the HISHSV system. The HIS was set to hop between two wavelengths at a rate of 300 frames per second (fps): 485.7 nm, the peak emission of aluminum monoxide, and 502.3nm, the bottom of the same emission peak. The second experiment images ignited AlO from the burn of an aluminized ammonium perchlorate solid propellant hoping between the wavelength of 486.3nm and 480.0nm at 2100 fps.

Three hyperspectral imaging systems have been implemented as microscopic hyperspectral imagers to support phenomenology studies at high spatial resolution. Wavelength ranges are VNIR (500 to 1000 nm), SWIR (1000 nm to 2500 nm) and LWIR (7 to 14 microns). The spatial resolution of the system at all wavelengths is 30 microns and covers a field of view of 8x30 mm. Samples are illuminated by rings of halogen lights and IR emitters for reflectance measurements and at thermal wavelengths can also be measured in thermal emission. Data are calibrated by scans of gold or Spectralon reflectance standards, and a flat plate blackbody for thermal emission measurements.

Hyperspectral imaging (HSI) systems can provide detection and identification of a variety of targets in the presence of complex backgrounds. However, current generation sensors are typically large, costly to field, do not usually operate in real time and have limited sensitivity and specificity. Despite these shortcomings, HSI-based intelligence has proven to be a valuable tool, thus resulting in increased demand for this type of technology. By moving the next generation of HSI technology into a more adaptive configuration, and a smaller and more cost effective form factor, HSI technologies can help maintain a competitive advantage for the U.S. armed forces as well as local, state and federal law enforcement agencies. Operating near the physical limits of HSI system capability is often necessary and very challenging, but is often enabled by rigorous modeling of detection performance. Specific performance envelopes we consistently strive to improve include: operating under low signal to background conditions; at higher and higher frame rates; and under less than ideal motion control scenarios. An adaptable, low cost, low footprint, standoff sensor architecture we have been maturing includes the use of conformal liquid crystal tunable filters (LCTFs). These Conformal Filters (CFs) are electro-optically tunable, multivariate HSI spectrometers that, when combined with Dual Polarization (DP) optics, produce optimized spectral passbands on demand, which can readily be reconfigured, to discriminate targets from complex backgrounds in real-time. With DARPA support, ChemImage Sensor Systems (CISS™) in collaboration with Research Triangle Institute (RTI) International are developing a novel, real-time, adaptable, compressive sensing short-wave infrared (SWIR) hyperspectral imaging technology called the Reconfigurable Conformal Imaging Sensor (RCIS) based on DP-CF technology. RCIS will address many shortcomings of current generation systems and offer improvements in operational agility and detection performance, while addressing sensor weight, form factor and cost needs. This paper discusses recent test and performance modeling results of a RCIS breadboard apparatus.

Pacific Advanced Technology (PAT) has developed an infrared hyperspectral camera, both MWIR and LWIR, small enough to serve as a payload on a miniature unmanned aerial vehicles. The optical system has been integrated into the cold-shield of the sensor enabling the small size and weight of the sensor.

This new and innovative approach to infrared hyperspectral imaging spectrometer uses micro-optics and will be explained in this paper. The micro-optics are made up of an area array of diffractive optical elements where each element is tuned to image a different spectral region on a common focal plane array. The lenslet array is embedded in the cold-shield of the sensor and actuated with a miniature piezo-electric motor. This approach enables rapid infrared spectral imaging with multiple spectral images collected and processed simultaneously each frame of the camera.

This paper will present our optical mechanical design approach which results in an infrared hyper-spectral imaging system that is small enough for a payload on a mini-UAV or commercial quadcopter.

The diffractive optical elements used in the lenslet array are blazed gratings where each lenslet is tuned for a different spectral bandpass. The lenslets are configured in an area array placed a few millimeters above the focal plane and embedded in the cold-shield to reduce the background signal normally associated with the optics.

We have developed various systems using a different number of lenslets in the area array. Depending on the size of the focal plane and the diameter of the lenslet array will determine the spatial resolution. A 2 x 2 lenslet array will image four different spectral images of the scene each frame and when coupled with a 512 x 512 focal plane array will give spatial resolution of 256 x 256 pixel each spectral image. Another system that we developed uses a 4 x 4 lenslet array on a 1024 x 1024 pixel element focal plane array which gives 16 spectral images of 256 x 256 pixel resolution each frame.

We propose a large aperture static imaging spectrometer (LASIS) based on planar lightwave circuit (PLC) MZI array. The imaging spectrometer works in the push-broom mode with the spectrum performed by interferometry. While the satellite/aircraft is orbiting, the same source, seen from the satellite/aircraft, moves across the aperture and enters different MZIs, while adjacent sources enter adjacent MZIs at the same time. The on-chip spectrometer consists of 256 input mode converters, followed by 256 MZIs with linearly increasing optical path delays and a detector array. Multiple chips are stick together to form the 2D image surface and receive light from the imaging lens. Two MZI arrays are proposed, one works in wavelength ranging from 500nm to 900nm with SiON(refractive index 1.6) waveguides and another ranging from 1100nm to 1700nm with SOI platform. To meet the requirements of imaging spectrometer applications, we choose large cross-section ridge waveguide to achieve polarization insensitive, maintain single mode propagation in broad spectrum and increase production tolerance. The SiON on-chip spectrometer has a spectral resolution of 80cm^-1 with a footprint of 17×15mm² and the SOI based on-chip spectrometer has a resolution of 38cm^-1 with a size of 22×19mm². The spectral and space resolution of the imaging spectrometer can be further improved by simply adding more MZIs. The on-chip waveguide MZI array based Fourier transform imaging spectrometer can provide a highly compact solution for remote sensing on unmanned aerial vehicles or satellites with advantages of small size, light weight, no moving parts and large input aperture.

NIR technology has been available for a long time, certainly more than 50 years. Without any doubt, it has found many niche applications, especially in the pharmaceutical, food, agriculture and other industries due to its flexibility. There are a number of advantages over other existing analytical technologies we can list, for example virtually no need for sample preparation; usually NIR does not demand sample destruction and subsequent discard; NIR provides fast results; NIR does not require extensive operator training and carries small operating costs. However, the key point about NIR technology is the fact that it´s more related to statistics than chemistry or, in other words, we are more concerned about analyzing and distinguishing features within the data than looking deep into the chemical entities themselves. A simple scan reading in the NIR range usually involves huge inflows of data points. Usually we decompose the signals into hundreds of predictor variables and use complex algorithms to predict classes or quantify specific content. NIR is all about math, especially by converting chemical information into numbers. Easier said than done. A NIR signal is a very complex one. Usually the signal responses are not specific to a particular material, rather, each group´s responses add up, thus providing low specificity of a spectral reading. This paper proposes a simple and efficient method to analyze and compare NIR spectra for the purpose of identifying the presence of active pharmaceutical ingredients in finished products using low cost NIR scanning devices connected to the internet cloud.

The miniaturization of unmanned systems and spacecraft, as well as computing and sensor technologies, has opened new opportunities in the areas of remote sensing and multi-sensor data fusion for a variety of applications. Remote sensing and data fusion historically have been the purview of large government organizations, such as the Department of Defense (DoD), National Aeronautics and Space Administration (NASA), and National Geospatial-Intelligence Agency (NGA) due to the high cost and complexity of developing, fielding, and operating such systems. However, miniaturized computers with high capacity processing capabilities, small and affordable sensors, and emerging, commercially available platforms such as UAS and CubeSats to carry such sensors, have allowed for a vast range of novel applications. In order to leverage these developments, Embry-Riddle Aeronautical University (ERAU) has developed an advanced sensor and data fusion laboratory to research component capabilities and their employment on a wide-range of autonomous, robotic, and transportation systems. This lab is unique in several ways, for example, it provides a traditional campus laboratory for students and faculty to model and test sensors in a range of scenarios, process multi-sensor data sets (both simulated and experimental), and analyze results. Moreover, such allows for “virtual” modeling, testing, and teaching capability reaching beyond the physical confines of the facility for use among ERAU Worldwide students and faculty located around the globe. Although other institutions such as Georgia Institute of Technology, Lockheed Martin, University of Dayton, and University of Central Florida have optical sensor laboratories, the ERAU virtual concept is the first such lab to expand to multispectral sensors and data fusion, while focusing on the data collection and data products and not on the manufacturing aspect. Further, the initiative is a unique effort among Embry-Riddle faculty to develop multi-disciplinary, cross-campus research to facilitate faculty- and student-driven research. Specifically, the ERAU Worldwide Campus, with locations across the globe and delivering curricula online, will be leveraged to provide novel approaches to remote sensor experimentation and simulation. The purpose of this paper and presentation is to present this new laboratory, research, education, and collaboration process.

There is a strong commercial incentive for characterizing power semiconductor devices during manufacture non-destructively. One area of concern are the stresses in the material introduced during manufacture by processes such as wafer thinning and chip separation. Raman spectroscopy can be used to measure stress in different semiconductor materials directly, non-destructively and quantitatively. Here, we describe Raman measurements on two semiconductor materials: silicon and silicon carbide. Measurements of silicon carbide are made on silicon carbide wafers; stress and material analyses of silicon are performed on: (i.) silicon wafers that had undergone different wafer thinning methods and (ii) along die sidewalls formed by mechanical and laser dicing. Our measurements demonstrate that micro-Raman spectroscopy is a feasible method for both measuring stress in thin wafers and for optimizing the thin wafer processes.

The development of reliable, high-power, frequency-tunable terahertz (THz)-wave sources is crucial for a wide variety of applications, such as spectroscopy, imaging, and sensing. In order to generate frequency-tunable THz waves at room temperature, one of the most promising methods is a wavelength conversion in nonlinear optical crystals. Here, we present our recent results on high-power, widely-tunable, frequency-agile THz-wave sources based on nonlinear parametric processes in MgO:LiNbO₃ crystals. By changing the noncollinear phase-matching condition in MgO:LiNbO₃, the tunability of sub-nanosecond-pumped injection-seeded THz-wave parametric generators (is-TPGs) covers the 3.65-octave frequency range from 0.37 THz up to 4.65 THz. The monochromatic THz-wave output from is-TPGs is greater than 10 kW peak power with the linewidth of approximately 3 GHz and the stability of 1%. These is-TPG systems are reliable and promising high-power tunable THz-wave sources for frequency-domain spectroscopic measurements towards THz sensing and detector sensitivity calibration.

We present a new satellite-based instrument concept that will enable global measurements of atmospheric temperature and humidity profiles with unprecedented resolution and accuracy, compared to currently planned missions. It will also provide global measurements of essential climate variables related to ice clouds that will better constrain global climate models. The instrument is enabled by the use of superconducting detectors coupled to superconducting filterbank spectrometers, operating between 50GHz and 850 GHz. We present the science drivers, the current instrument concept and status, and predicted performance.

In this study, we present a classification algorithm for terahertz time-domain spectroscopy systems (THz-TDS) that can be trained to identify most commonly used explosives (C4, HMX, RDX, PETN, TNT, composition-B and blackpowder) and some non-explosive samples (lactose, sucrose, PABA). Our procedure can be used in any THz-TDS system that detects either transmission or reflection spectra at room conditions. After preprocessing the signal in low THz regime (0.1 – 3 THz), our algorithm takes advantages of a latent space transformation based on principle component analysis in order to classify explosives with low false alarm rate.

Silicon carbide (SiC) is a wide band-gap semi-conductor material that is used increasingly for high voltage power devices, since it has a higher breakdown field strength and better thermal conductivity than silicon. However, in particular its hardness makes wafer processing difficult and many standard semi-conductor processes have to be specially adapted. We measure the effects of (i) mechanical processing (i.e. grinding of the backside) and (ii) chemical and thermal processing (i.e. doping and annealing), using confocal microscopy to measure the surface roughness of ground wafers and micro-Raman spectroscopy to measure the stresses induced in the wafers by grinding. 4H-SiC wafers with different dopings were studied before and after annealing, using depth-resolved micro-Raman spectroscopy to observe how doping and annealing affect: i.) the damage and stresses induced on the crystalline structure of the samples and ii.) the concentration of free electrical carriers. Our results show that mechanical, chemical and thermal processing techniques have effects on this semiconductor material that can be observed and characterized using confocal microscopy and high resolution micro Raman spectroscopy.

Remote sensing has moved out of the laboratory and into the real world. Instruments using reflection or Raman imaging modalities become faster, cheaper and more powerful annually. Enabling technologies include virtual slit spectrometer design, high power multimode diode lasers, fast open-loop scanning systems, low-noise IR-sensitive array detectors and low-cost computers with touchscreen interfaces. High-volume manufacturing assembles these components into inexpensive portable or handheld devices that make possible sophisticated decision-making based on robust data analytics. Examples include threat, hazmat and narcotics detection; remote gas sensing; biophotonic screening; environmental remediation and a host of other applications.