Hyperspectral imaging has emerged as a powerful tool for semiconductor analysis, combining spatial and spectral information to provide detailed insights into material properties. Unlike conventional imaging techniques, hyperspectral methods capture a full spectrum at each pixel, enabling precise mapping of electronic, vibrational, and thermal characteristics. Key techniques in this domain include photoluminescence (PL) imaging, Raman mapping, and infrared (IR) microscopy, each offering unique advantages for defect localization, material homogeneity assessment, and performance evaluation.

Photoluminescence imaging is widely used to study electronic properties and defect states in semiconductors. When a semiconductor is excited by a laser or other light source, electron-hole pairs are generated, and their recombination emits light at specific wavelengths. PL imaging captures this emission across a sample, mapping variations in bandgap energy, doping concentrations, and defect densities. For instance, in silicon or III-V materials, dislocations and impurities create non-radiative recombination centers, which appear as dark spots in PL maps. The spatial resolution of PL imaging is diffraction-limited, typically around 200-300 nm for visible light, but can be enhanced with near-field techniques. Spectral resolution depends on the detector and grating system, often achieving sub-nanometer precision. Data processing involves background subtraction, peak fitting, and spectral deconvolution to isolate contributions from different recombination pathways.

Raman mapping provides complementary information by probing vibrational modes in the crystal lattice. When light interacts with a material, inelastic scattering produces shifts in wavelength corresponding to phonon energies. Raman spectra reveal crystal structure, strain, composition, and defects. For example, in silicon, the Raman peak at 520 cm-1 shifts under mechanical stress, allowing strain mapping with micron-scale resolution. In layered materials like graphene or transition metal dichalcogenides, Raman signals indicate layer thickness, doping, and defects. The spatial resolution of Raman microscopy is also diffraction-limited, usually around 500 nm for visible lasers, though tip-enhanced Raman spectroscopy (TERS) can achieve nanometer-scale resolution. Data processing includes baseline correction, peak fitting, and multivariate analysis such as principal component analysis (PCA) to identify spatial patterns in complex spectra.

Infrared microscopy extends hyperspectral analysis to longer wavelengths, probing molecular vibrations and free-carrier absorption. Fourier-transform infrared (FTIR) spectroscopy is particularly useful for organic semiconductors, oxide materials, and thin films. In organic photovoltaics, IR absorption maps reveal phase segregation and crystallinity variations. For silicon-based devices, free-carrier absorption can map doping concentrations with micron-scale resolution. The spatial resolution of IR microscopy is lower than visible techniques, typically 2-10 µm, due to the longer wavelengths involved. However, synchrotron-based IR sources can improve this to sub-micron levels. Data processing often involves absorbance calculations, spectral normalization, and chemometric methods to extract relevant material parameters.

The integration of these hyperspectral techniques enables comprehensive semiconductor analysis. For defect localization, correlative PL and Raman mapping can distinguish electronic defects from structural imperfections. In material homogeneity studies, multivariate analysis of hyperspectral data identifies spatial variations in composition, strain, or doping that may impact device performance. For example, in perovskite solar cells, combined PL and Raman maps have revealed correlations between halide segregation and non-radiative losses. In wide-bandgap materials like GaN or SiC, hyperspectral imaging detects dislocations and point defects that limit breakdown voltage in power devices.

Resolution limits are a critical consideration in hyperspectral imaging. While diffraction sets a fundamental boundary, practical factors such as signal-to-noise ratio, detector sensitivity, and scan speed also influence achievable resolution. For PL and Raman, confocal setups improve spatial resolution by rejecting out-of-focus light, but at the cost of longer acquisition times. Super-resolution techniques, such as stochastic optical reconstruction microscopy (STORM) or structured illumination, have been adapted for PL imaging to break the diffraction limit. In IR microscopy, the use of focal plane array detectors enables faster mapping but sacrifices some spectral resolution compared to single-point detectors.

Data processing methods are equally important for extracting meaningful information from hyperspectral datasets. Raw data often contains noise, background signals, and overlapping spectral features that must be disentangled. Common preprocessing steps include dark current subtraction, flat-field correction, and spectral smoothing. For PL data, fitting algorithms like multi-Gaussian models separate contributions from band-edge emission, defect states, and excitonic features. Raman data may require advanced algorithms such as non-negative matrix factorization (NMF) to deconvolve overlapping peaks. Machine learning approaches, including neural networks and support vector machines, are increasingly used to classify defects or predict material properties from hyperspectral data.

Applications in defect localization benefit from the high sensitivity of hyperspectral techniques to minute variations in material properties. In silicon wafers, PL imaging can detect metal impurities at concentrations as low as 10^10 atoms/cm³. Raman mapping identifies micro-cracks and residual stress in processed devices, which are critical for reliability. IR microscopy reveals moisture ingress or delamination in packaged devices. These capabilities are vital for failure analysis and process optimization in semiconductor manufacturing.

Material homogeneity studies leverage hyperspectral imaging to ensure consistent performance across large-area samples. In thin-film photovoltaics, spatial variations in bandgap or doping can lead to efficiency losses. Hyperspectral maps guide process adjustments to minimize these variations. For 2D materials, Raman and PL imaging assess uniformity in layer thickness and defect density, which are crucial for electronic applications. In compound semiconductors like GaAs or InP, hyperspectral data informs epitaxial growth conditions to achieve desired compositional profiles.

In summary, hyperspectral imaging provides a versatile and non-destructive approach to semiconductor analysis, combining PL, Raman, and IR techniques to address diverse challenges in material characterization. Resolution limits are dictated by diffraction but can be enhanced with advanced optical methods. Data processing relies on a combination of traditional algorithms and emerging machine learning tools. Applications span defect localization, homogeneity assessment, and device optimization, making hyperspectral imaging indispensable for modern semiconductor research and development.