Efficient and sustainable production, recovery and recycling phases of semiconductors (SC) life cycles require noninvasive, inline methods able to identify their composition in material streams. Ideally, the sensor system should be fast and incorporated into conveyor-belt operations. Rapid identification as well as spatial distribution maps would allow for real-time monitoring and quality control of the material stream. Considering these requirements, we suggest the sequential use of fast hyperspectral reflectance imaging (HSI) and Raman spectroscopic sensors for the identification of SC types in a sensor network configuration. We propose spectral proxies based on electronic properties derived from HSI-reflectance (i.e. absorption edge linked to the band gap values) and Raman sensors (i.e. Raman-active phonon modes) for SC identification. We identify potential limitations of each proxy on identifying undoped/doped SC materials, and discuss which process workflows enable optimized SC classification. We demonstrate the multi-sensor approach with SC standards (GaAs, GaSb, InP, 4H-SiC, and Borosilicate) which are relevant for both opto- and power-electronic devices, and showcase the potential of sequential data acquisition by fast HSI-reflectance sensors in the visible to shortwave-infrared (integration times: (4.5–18) ms) and Raman scattering (excitation laser: 532 nm, acquisition times: (0.5–10) s).
Deep learning techniques are commonly utilized to tackle various computer vision problems, including recognition, segmentation, and classification from RGB images. With the availability of a diverse range of sensors, industry-specific datasets are acquired to address specific challenges. These collected datasets have varied modalities, indicating that the images possess distinct channel numbers and pixel values that have different interpretations. Implementing deep learning methods to attain optimal outcomes on such multimodal data is a complicated procedure. To enhance the performance of classification tasks in this scenario, one feasible approach is to employ a data fusion technique. Data fusion aims to use all the available information from all sensors and integrate them to obtain an optimal outcome. This paper investigates early fusion, intermediate fusion, and late fusion in deep learning models for bulky waste image classification. For training and evaluation of the models, a multimodal dataset is used. The dataset consists of RGB, hyperspectral Near Infrared (NIR), Thermography, and Terahertz images of bulky waste. The results of this work show that multimodal sensor fusion can enhance classification accuracy compared to a single-sensor approach for the used dataset. Hereby, late fusion performed the best with an accuracy of 0.921 compared to intermediate and early fusion, on our test data.
Waste from electronic equipment (WEEE) is a fast-growing complex waste stream, and plastics represent around 25% of its total. The proper recycling of plastics from WEEE depends on the identification of polymers prior to entering the recycling chain. Technologies aiming for this identification must be compatible with conveyor belt operations and fast data acquisition. Therefore, we selected three promising sensor types to investigate the potential of optical spectroscopy-based methods for identification of plastic constituents in WEEE. Reflectance information is obtained using Hyperspectral cameras (HSI) in the short-wave infrared (SWIR) and mid-wave infrared (MWIR). Raman point acquisitions are well-suited for specific plastic identification (532 nm excitation). Integration times varied according to the capabilities of each sensor, never exceeding 2 seconds. We have selected 23 polymers commonly found in WEEE (PE, PP, PVC ABS, PC, PS, PTFE, PMMA), recognising spectral fingerprints for each material according to literature reports. Spectral fingerprint identification was possible for 60% of the samples using SWIR-HSI; however, it failed to produce positive results for black plastics. Additional information from MWIR-HSI was used to identify two black samples (70% identified using SWIR + MWIR). Fingerprint assignment in shorttime Raman acquisition (1 -2 seconds) was successful for all samples. Combined with the efficient mapping capabilities of HSI at time scales of milliseconds, further developments promise great potential for fast-paced recycling environments. Furthermore, integrated solutions enable increased accuracy (cross-validations) and hence, we recommend a combination of at least 2 sensors (SWIR + Raman or MWIR + Raman) for recycling activities.
The application of drill core hyperspectral data in exploration campaigns is receiving great interest to obtain a general overview of a mineral deposit. However, the main approach to the investigation of such data is by visual interpretation, which is subjective and time consuming. To address this issue, recently, the use of machine learning techniques is proposed for the analysis of this data. For drill core samples that for which only very little prior knowledge is often available, applying classification algorithms which are supervised learning methods is very challenging. In this paper, we suggest to use clustering (unsupervised) methods for mineral mapping, which are similar to classification but no predefined class labels are needed. To handle mapping of the very highly mixed pixels in drill core hyperspectral data, we propose to use advance subspace clustering methods, in which pixels are assumed to lie in a union of low-dimensional subspaces. We conduct a comparative study and evaluate the performance of two well-known subspace clustering methods, i.e., sparse subspace clustering (SSC) and low rank representation (LRR). For the experiments, we acquired VNIR-SWIR hyperspectral data and applied scanning electron microscopy based Mineral Liberation Analysis (MLA) for two drill core samples. MLA is a high resolution imaging technique that allows detailed mineral charactrisition. We use the high-resolution MLA image as a reference to analyse the clustering results. Qualitative analysis of the obtained clustering maps indicate that the subspace clustering methods can accurately map the available minerals in the drill core hyperspectral data, especially in comparison to the traditional k-means clustering method.
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