Semiconductor, packaging & electronics

Reticle & photomask defect review

Locate missing features, pinholes, particles, and pattern deviations in reticle and photomask images.

ModalitiesBrightfield mask inspectionDarkfield inspectionMicroscopy
Illustrative photomask pattern field with a missing feature and particle defects
9
Findings
2
Pinholes
64
Fields

Example outputs shown for illustration. Numbers depend on your samples and protocol.

Image: Illustrative rendering (AI-generated, gpt-image-2), not an inspection image of a specific mask

Brightfield mask inspection
Reticle pattern fields captured against a reference.
Darkfield inspection
Scattered-light capture that lifts particles and pinholes.
Microscopy
Bench microscopy at the magnification your protocol already uses.

Results

Each measurement below comes from your own images, tied back to the annotated frame it came from, so results stay comparable across samples, locations, and conditions.

per area

Defect location

Findings placed in sample coordinates to reveal spatial patterns and local density.

Illustrative shape
Bottom
64%
Corner
71%

In your report: Coordinate-referenced map, regional density, and representative images.

class

Pattern-deviation class

Each detected feature assigned to a class using the agreed rule set, shown on the image.

Illustrative shape
<1µm
1-2µm
2-4µm
>4µm

In your report: Per-feature class, counts by class, and annotated overlays.

µm or count

Particle / pinhole count

Size and shape measured for every detected object, then summarized across the population.

Pinholes2

Example, not a claimed result

In your report: Per-object table, size distribution, and summary percentiles.

µm or count

Size distribution

Size and shape measured for every detected object, then summarized across the population.

Illustrative shape

In your report: Per-object table, size distribution, and summary percentiles.

Built around your image set

Use the calibrated images your team already collects, together with the locations you need to compare.

01
Representative images
Provide representative brightfield mask inspection images with a recorded scale calibration and the regions of interest clearly visible.
02
Image capture details
Record instrument, magnification, pixel scale, preparation method, and the smallest feature the review must resolve.
03
What to compare
State the samples, number of fields, and conditions to compare. A single field of view is not treated as a whole-sample result by itself.

Each report includes

The artifacts your team receives, ready for review and archive.

Annotated image set

Original images with regions of interest, measurement points, masks, and finding overlays.

Measurement table

Location-indexed values, units, distributions, and QC flags in a structured export.

Method record

Calibration evidence, analysis settings, version history, and validation summary.

Confidence in every resultTraceable measurements, reviewed against your agreed reference method.
Traceable scale
The report records the image scale, calibration evidence, and a method-specific expanded measurement uncertainty. It does not use one product-wide accuracy number.
Reference comparison
The configured method is compared with your accepted reference method or reviewed annotations, and reports bias by measurement range and image condition.
Detection performance
Detections are evaluated against reviewed reference regions with precision, recall, and segmentation overlap, separated by the conditions that affect performance.
Repeatability
The locked protocol is rerun on the same inputs and on a defined repeat set. The review records variation from image acquisition, sampling, and analysis separately where possible.

What this result does not establish: Supports image-based defect review. Mask disposition, printability, and any repair decision require the qualified inspection and lithography process used by your organization.

The measurement, today

Mask defects are often reviewed against reference imagery by hand. A finding may be obvious in a field yet difficult to quantify, classify, and retain for later comparison.

What it costs

A reticle defect can print repeatedly across a wafer. Early image review helps teams separate candidates for engineering review from cosmetic or imaging artifacts.

From image to reviewed result

  1. 1

    Load inspection fields

    Provide brightfield, darkfield, or microscope images with the reference pattern or a known-good field.

  2. 2

    Align the pattern

    Register repeated structures so missing, extra, and displaced features can be localized.

  3. 3

    Classify candidates

    Separate pattern deviations, pinholes, opaque particles, and image artifacts for review.

  4. 4

    Export review evidence

    Deliver a location-indexed list with the original and annotated inspection fields.

Talk to a scientist

Not sure this is the right measurement?

Send a representative image and your measurement goal. A ConductVision scientist will confirm whether this is the right fit, or point you to the closer workflow, before you commit to a quote.

Share your image set
One representative image is enough to start the conversation.
Book a 30-minute review
Walk through the measurement plan and confidence evidence live.

Related applications

Cross-section SEM of dense resist lines leaning and merging at tight pitch, showing pattern collapse

Photoresist pattern defects

Detect resist pattern collapse, bridging, footing, and scumming after develop.

Top-down SEM of nanoscale silicon reference lines used for critical-dimension metrology

Critical dimension (CD) metrology

Line width, space, and via diameter measured from top-down images, with per-feature tables.

Wafer inspection field with die grid and defect pattern clusters

Semiconductor defect patterns

Classify wafer-map patterns and quantify defect clusters and densities.

Send a sample image and a measurement goal

We will show the closest ConductVision workflow and flag what needs custom validation for your images.