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).
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.
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