Insightek vs manual / microscope inspection
Manual inspection reads what an experienced operator can see under a microscope — mostly by sampling, and mostly by subjective judgment. Insightek replaces sampling with 100% inspection and subjective judgment with quantified, verifiable data. This comparison reflects general differences of typical configurations, not any specific setup; actual capability is subject to on-site validation.
Manual / microscope inspection
Operators inspecting micro-holes, post-dicing edges, or test-pin arrays under a microscope — typically by sampling, recorded on paper or spreadsheets.
Insightek
AI inspection and metrology on one platform: high-resolution imaging with high-speed stitching, dedicated models, and micron metrology — with every verdict traceable.
Where manual / microscope inspection still fits
We are not out to remove every operator. Some situations are still best served at the bench, and the first sample sets any automated system learns from come from exactly this work.
- A first look at a brand-new process or defect type nobody has imaged yet
- Very low volume, where a few sampled parts a day is genuinely enough
- Building the first good / defect / borderline sample sets an automated system will learn from
- Offline spot checks and second opinions during early feasibility work
Where Insightek changes the outcome
These are the patterns where sampling and subjective judgment cost you escapes, and quantified 100% inspection pays off.
- You need in-line 100% inspection instead of sampling — every hole, edge, or pin
- Tiny or low-contrast defects that are escape-prone by eye
- Real-vs-false defect judgment that has to be consistent shift to shift
- Micron-level metrology and pass/fail on the same part, in one output
- A data loop — coordinates, size, defect class, trend — that feeds MES / SPC
Capability comparison
Five dimensions drawn from typical configurations. This reflects general differences, not any specific setup; actual capability is subject to on-site validation on your samples.
Coverage & detection
Coverage
Mostly sampling, limited coverage
In-line 100%; speed scales with compute
Tiny / low-contrast defects
Experience-based, escape-prone
Dedicated models tuned for small defects and complex textures
Judgment & metrology
Real-vs-false discrimination
Subjective, operator-dependent
AI verdicts with sample-set acceptance, consistent
Metrology capability
Hard to quantify
Inspection plus micron metrology in one output
Data & traceability
Data loop
Paper / spreadsheet records
Coordinates, size, class, trend + MES / SPC link
From spot checks to 100% inspection
Adoption is phased, and each step has a deliverable. You can start small — software and offline validation first, an integrated unit next, standard equipment last.
- 01
1 · Sample & process review
We review your samples and process together. Deliverable: a feasibility conclusion.
- 02
2 · Imaging & algorithm validation
We validate imaging and the algorithm on your parts. Deliverable: a demo report.
- 03
3 · Inspection items & thresholds
We define inspection items and thresholds with you. Deliverable: a verdict scheme and acceptance criteria.
- 04
4 · Line-side integration
We integrate at the line. Deliverable: an integration validation report.
- 05
5 · Acceptance & replication
We run acceptance and prepare to replicate. Deliverable: an acceptance report and a maintenance plan.
Frequently asked
Do we have to switch everything to 100% inspection at once?
How many samples do you need to start?
How do you prove the verdicts are reliable?
What data do we get out?
Bring your samples and your escapes.
A technical review of your current sampling coverage, escape risk, and data gaps. The first deliverable is a feasibility conclusion, not a quote.