A Precision Enhancement Deep Learning Framework for Package Substrate Defect Detection

ASE's three-network defect-detection framework reached 93.21% classification accuracy and cut human inspection workload by 28% — while holding the underkill rate, the metric that decides whether bad parts escape, to 2.8%. Those numbers come from an experiment on ASE's own manufacturing dataset, reported by Yi-Chung Hsu and Wong-Shian Huang, and they matter because automated optical inspection has a stubborn failure mode: the defects a model has never seen are exactly the ones it lets through.

Why AOI Alone Still Needs a Human

Pairing automated optical inspection (AOI) equipment with deep neural network (DNN) defect prediction has already removed a large share of the manual effort in production inspection. The problem surfaces during substrate manufacturing, where shifts in in-process conditions produce defects that don't match the training set — combinations of multiple defects, or anomalies no operator has catalogued. Mainstream DNN object detection (OD) methods have not reached the accuracy needed to catch these cases on the line, so human visual inspection remains essential as a backstop. That backstop is expensive: it is slow, it varies between inspectors, and it does not scale with volume.

The engineering goal, then, is not simply "more accurate detection." It is a framework that reduces how often a human has to look while keeping the escape rate of defective product low enough to trust downstream.

ASE's answer integrates three DNN methods, each compensating for a weakness in the others.

Network	Role	How it works
Object detection (OD)	Locate and classify defects	Constructs hyperplanes for bounding-box objects and their classes
Anomaly detection (AD)	Catch the unfamiliar	Measures distances between embedding vectors trained on normal samples to flag anomalies
Auxiliary classification (AC)	Sharpen the verdict	Centers the OD-proposed object in a cropped image and re-judges its class, refining OD's anchor-based training

The design logic is what makes this more than an ensemble. Object detection is strong at finding and labeling defects it has been trained on, but weak on novel patterns. Anomaly detection covers that gap precisely because it is trained only on normal samples — anything that sits far from the normal embedding cluster is flagged regardless of whether that defect type was ever labeled. Auxiliary classification then addresses OD's precision problem: by cropping to the proposed defect, centering it, and re-classifying, AC refines the anchor-based training that anchors OD's accuracy. Together, the three move detection from "good at known defects" toward "robust against unknown ones."

What the Results Mean on the Line

On ASE's manufacturing dataset, the framework delivered three results that map directly to factory economics:

Metric	Result	Why it matters
Human examination reduction	28%	Less manual inspection labor per lot
Underkill rate	2.8%	Few defective units escape to downstream processes
Classification accuracy (OD + AC)	93.21% vs 89.74% mainstream	More defects correctly typed for root-cause action

The combination of high classification accuracy and a low underkill rate is the point. A model can always reduce human workload by simply passing more parts — but that raises escapes. Holding underkill at 2.8% while removing more than a quarter of the manual inspection load means the framework reduces cost and maintains the quality of normal product flowing to downstream processes at the same time. The 93.21% classification accuracy, surpassing the mainstream 89.74%, also means defects are not just caught but correctly categorized, which is what feeds process engineers the signal they need to fix the root cause rather than just scrap the part.

How This Fits ASE's Smart Manufacturing

Defect detection is one node in a much larger AI-driven manufacturing system. As the world's largest outsourced semiconductor assembly and test (OSAT) provider, ASE applies machine learning and deep learning across predictive maintenance, quality assurance, process-parameter optimization, robotic process automation, and supply-chain forecasting. Quality assurance — the practice this framework belongs to — uses supervised object detection and segmentation alongside unsupervised anomaly detection to locate and classify defects in real time and remove abnormal units automatically, a level of consistency manual sampling cannot match.

That places this paper inside a broader pattern: a semiconductor company that builds the chips powering modern AI is also consuming AI to run the fabs that build them. ASE's deployment record — dozens of lights-out factories and membership in the World Economic Forum's Global Lighthouse Network — is the production context that turns a research framework like this one into a line-ready capability rather than a benchmark result.

What Comes Next

A defect-detection framework that combines object detection, anomaly detection, and auxiliary classification gives a smart factory a path to keep reducing manual inspection without raising escape risk — and to keep working as new, unfamiliar defect patterns appear, which is the failure mode that breaks single-model AOI. For ASE, capabilities proven on its own substrate lines feed directly back into more consistent, lower-cost packaging and test for customers, turning factory-floor AI into measurable yield and quality advantages.

Care about yield and inspection quality in advanced packaging? Explore how ASE's AI-driven smart manufacturing delivers consistency at production scale at ase.aseglobal.com.

Frequently Asked Questions

Q: What is automated optical inspection (AOI) in semiconductor manufacturing? A: AOI uses cameras and image analysis to inspect parts for defects automatically, replacing much of the manual visual inspection on a production line. When paired with deep neural network (DNN) defect prediction, AOI significantly reduces manual effort, but it can struggle with novel or combined defects that don't appear in its training data, which is why a human backstop has traditionally remained necessary.

Q: How does ASE's defect-detection framework improve accuracy? A: It integrates three deep-learning networks: object detection (OD) to locate and classify known defects, anomaly detection (AD) to flag unfamiliar patterns by measuring distance from normal-sample embeddings, and auxiliary classification (AC) to re-judge each detected object after centering it in a crop. Combining OD and AC reached 93.21% classification accuracy, surpassing the mainstream 89.74%.

Q: What is an underkill rate and why does it matter? A: The underkill rate is the share of defective units that pass inspection and escape to downstream processes. It is the metric that determines inspection trustworthiness — a low rate means few bad parts get through. ASE's framework held underkill to 2.8% while reducing human examination by 28%.

Q: How much did the framework reduce manual inspection? A: On ASE's manufacturing dataset, the framework reduced human examination by 28% while maintaining a 2.8% underkill rate, lowering inspection labor without raising the risk of defective product escaping downstream.

Q: Why combine anomaly detection with object detection? A: Object detection is accurate on defects it was trained on but weak on novel patterns. Anomaly detection is trained only on normal samples, so it flags anything far from the normal embedding cluster regardless of whether that defect type was labeled. Combining them lets the framework catch unfamiliar defects that single-model object detection would miss.

✏️ AI 標題改寫建議

原始標題： A Precision Enhancement Deep Learning Framework for Package Substrate Defect Detection

建議標題： 93.21% Accuracy, 2.8% Underkill: How ASE's Three-Network AI Catches the Defects AOI Misses

改寫理由： 原始標題為學術命名，缺少量化鉤子與讀者利益。建議標題前置最具說服力的兩個數據（93.21% 準確率、2.8% underkill），點明痛點（AOI 漏檢）與解法（三網路 AI），並涵蓋「AI defect detection」高搜尋意圖關鍵字。依 skill 規則，Ghost 文章標題沿用原始標題，本建議僅供編輯團隊參考。

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