Beyond Visible: How SWIR + ViSWIR Lenses Unlock Hidden Signals for Machine Vision

The Case for Integrating SWIR into Your Next Vision System

If you work in machine vision, you’ve probably felt the limits of visible-light imaging—glare, opaque housings, look‑alike materials, or fog that swallows contrast. Short-Wave Infrared (SWIR), especially when paired with InGaAs sensors and purpose-built optics like Computar’s ViSWIR series, gives us a different kind of signal: contrast from chemistry, transmission through silicon and hard plastics, and resilience against haze. Think of it as turning on a new layer in the scene—suddenly, the “invisible” becomes a dataset.

Semiconductors and electronics: seeing through silicon

• Wafer inspection: Silicon becomes transparent beyond ~1100 nm, so SWIR can look through bonded wafers and down to buried layers to reveal particles, voids, and alignment problems that visible cameras can’t see.
• Component analysis: Photo-emission failure analysis on ICs and overlay checks on MEMS benefit from SWIR sensitivity and low background clutter, turning faint events into actionable signals.
• Industrial manufacturing and recycling: materials, not colors
• Material sorting: SWIR encodes chemistry. Separating PET from PVC and other polymers becomes robust because absorption bands differ, enabling high-throughput, low-false-positive sorting on conveyors.
• Quality control through opacity: Hard plastic bottles and molded housings are partially transmissive in SWIR, enabling non-destructive checks—fill level, presence/absence, and correct product verification—without cracking a seal.

Food, agriculture, and pharma: better contrast from biology

• Food inspection: Foreign-object detection and quality grading (including fat/water content proxies) become more reliable. In packaged goods, SWIR complements X‑ray by catching what density-based methods miss.
• Field and greenhouse agriculture: Vegetation indices in SWIR highlight water stress, disease onset, and canopy structure beyond what NIR alone can show, improving scouting and variable-rate decisions.
• Pharmaceutical workflows: From blister-pack verification behind printed foils to spectral cues for drug identification, SWIR adds authentication and process control where color fails.
• Security, defense, and surveillance: cut through the atmosphere
• Environmental penetration: Fog, smoke, and haze scatter visible light heavily. SWIR often keeps contrast and is used for coastal monitoring and long-range observation.
• Operational uses: Target ID, beam-riding and guided munition support, and covert free-space comms (laser-based, device-to-device) leverage SWIR wavelengths for lower signature and better propagation.
• Program alignment: Government and defense programs list SWIR as a core modality for day/night persistence and ISR workloads.

Medical and life sciences: deeper, gentler imaging

• Research microscopy and mesoscopy: Reduced scattering and autofluorescence in the SWIR window improve depth and SNR for observing structures and dynamics in tissue.
• In vivo fluorescence: With SWIR fluorophores, tissue absorption is lower, so emission travels farther with higher contrast—useful for small‑animal work and translational imaging pipelines.

Other high‑leverage domains

• ITS and UAVs: Traffic systems and autonomous platforms exploit SWIR for headlight/glare suppression, adverse‑weather resilience, and material discrimination on the move.
• Anti‑counterfeiting: Hidden inks, pigments, and laminate features pop in SWIR, enabling quick authentication for currency, documents, art, and security labels.
• Remote sensing: Geological and environmental monitoring benefit from spectral bands that track moisture, mineralogy, and soil composition.
• Practical notes for deploying SWIR

Optics matter: Use lenses corrected across the visible-to-SWIR band to avoid focus shift and chromatic blur when you mix broadband or multispectral illumination.

• Illumination strategy: Pick narrow bands around diagnostic absorption features; LEDs and lasers in the 1150–1700 nm range are standard. For speed, pair strobed SWIR lighting with global-shutter InGaAs sensors.
• Calibration & reference: Include dark/white references and temperature stabilization—InGaAs dark current drifts. For spectral tasks, capture a few bands rather than the whole cube to keep throughput high.
• Data pipeline: SWIR’s lower photon flux benefits from denoising and HDR merges; deploy on‑sensor ROI and FPGA prefilters to safeguard line rates.

When SWIR outperforms visible

• The need to see through silicon, polymer housings, coated glass, or printed foils
• The need to separate look‑alike materials by chemistry (plastics, foods, minerals)
• The need for range and contrast in haze, fog, smoke, or low sun glare
• The need for deeper tissue contrast with reduced scattering

Why ViSWIR lenses + InGaAs sensors pair well

• Broadband correction: Designed to hold focus and MTF from visible into SWIR, simplifying mixed-spectrum systems.
• Low stray light: Coatings and design suppress flare, which would otherwise bury weak SWIR contrast.
• Mechanical stability: Industrial housings maintain back focus with temperature swings—important because InGaAs systems often run warm.

Getting started

• Identify the material contrast you actually need (water, lipids, silicon transparency, pigment signatures) and choose bands accordingly.
• Prototype with a tunable SWIR illuminator or a small set of bandpass filters; lock down two to four wavelengths that maximize separability.
• Match pixel size to your photon budget; don’t be afraid of slightly larger pixels in SWIR to keep SNR healthy.
• Validate on representative parts in their real packaging and under realistic haze/lighting.

The first time you swap a visible camera for SWIR, the scene may feel “empty” until you adjust the illumination and use the correct optics—then features leap out. That’s the trick: SWIR isn’t just another camera; it’s a different contrast universe. When you align optics, wavelengths, and processing, it turns challenging applications—like seeing through silicon or telling PET from PVC—into routine QA signals.

Contact us for more information on SWIR imaging, Computar’s ViSWIR series, and help choosing the right lens for your application.

Sources:

1. https://www.imveurope.com/article/swir-machine-vision-how-overcome-invisible-challenge
2. https://analyticalscience.wiley.com/
3. https://pmc.ncbi.nlm.nih.gov/articles/PMC6131259/
4. https://www.photonics.com/Articles/Multispectral_and_Hyperspectral_Imaging/a70129
5. http://sciencedirect.com/science/article/abs/pii/B9780443214936000058#:~:text=In%20the%20food%20industry%2C%20multispectral%20cameras%20play,affect%20the%20quality%20of%20the%20final%20product.
6. https://www.prophotonix.com/case-studies/multispectral-imaging-in-food-sorting/#:~:text=Customer%20Requirements,consistency%20of%20foreign%20matter%20detection