ASE Holdings · Semiconductors
AI integration case
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Explore the workflow
SOLUTION · MANUFACTURING
AI for manufacturing inspection and operations
Evaluate inspection, equipment knowledge or planning on actual plant data. Build around the line’s speed, hardware, interfaces and quality standards.
Validate on the line you actually run
Lighting, product variation, line speed and sample quality determine whether an inspection model is useful. We work with production and quality teams to choose one task and establish a baseline.
We test the model and integration under those conditions. Edge deployment is assessed where connectivity or data restrictions require it. Error limits, safe operating behavior and responsibility are agreed before any wider rollout.
- Use actual operating conditions
- Line samples
- Agreed with quality reviewers
- Error limits
- Assessed against system requirements
- Edge option
Capabilities
- Inspection on actual samples
- Evaluate defect detection with representative accepted and rejected parts under line conditions.
- False-positive and miss limits
- Compare thresholds and error rates with the quality team’s acceptance criteria.
- Line-system integration
- Assess MES, PLC or other available interfaces, including safe behavior when components fail.
- Deployment and operating records
- Choose edge or cloud deployment to fit requirements, with monitoring, versions and an operating runbook.
Use cases
- Visual inspection
- Assess detection for a defined defect set and product family before increasing coverage.
- Equipment knowledge lookup
- Retrieve the applicable manual, work instruction or repair record with its source version.
- Planning support
- Compare scheduling options using available orders, equipment and workforce constraints.
Delivery cadence
01 · SCOPE
Set the task and baseline
Agree on the product family, data samples, line limits and quality criteria.
02 · BUILD
Test the model and interface
Build the first model and integration, then check performance under actual operating conditions.
03 · VALIDATE
Compare with quality review
Measure misses, false rejects, speed and failure behavior against the baseline.
04 · OPERATE
Confirm the rollout plan
Agree on operating responsibility and evaluate new lines separately before replication.
FAQ
Can inspection work without external connectivity?
Local edge inference is an option where the task and hardware support it. We test dependencies, storage and failure behavior before defining the offline operating scope.
How do you prevent false rejects from slowing the line?
Evaluate thresholds on representative data and agree acceptable misses, false rejects and processing time with quality and production. The tradeoff is documented rather than hidden in one accuracy number.
What if we lack labeled defect data?
First assess available samples and the defect definitions. Data collection and labeling may be required; the pilot scope states what can be evaluated with the current evidence.
Can a system be copied to other lines?
The model, data specification and deployment process can be reused where appropriate. Each new line still needs validation for its products, lighting, equipment and operating conditions.
TENTEN × DIFY
AI connected to existing systems, with clear working boundaries
From Tenten’s October 2026 consulting deck: five client platforms, their AI integration workflows, data sources and human review.
Start with one workflow.
Agree on the next step.
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