ACI OS On-Premises: Secure Data for Advanced Packaging Rare Defects

In advanced semiconductor packaging, DaoAI ACI OS leverages its visual foundation model for feature recognition, 5-minute 0-code programming with one good sample, APDT positive/few-shot learning (1-20 good samples), and semantic false positive filtering. Through 100% on-premises private deployment, it successfully reduced the rare defect escape rate at a leading semiconductor manufacturer from a traditional 2.5% to <0.3% in advanced packaging, while ensuring absolute security of sensitive production data and effectively addressing the challenge of identifying rare defects with limited samples.

Advanced packaging, as a key technology extending Moore's Law, continuously breaks through in integration, performance, and power consumption. However, its complex multi-layer stacking, micron-level interconnections, and heterogeneous integration also bring challenges in identifying rare defects that traditional inspection solutions struggle with. Especially those low-frequency but high-impact occasional defects, such as micro-cracks, foreign objects, voids, and insufficient solder joints, are difficult for statistical or rule-based traditional AOI to cover effectively due to insufficient sample data. Manual visual inspection, on the other hand, faces issues like low efficiency, poor consistency, and data leakage risks. In the current context where high-precision assembly of automotive components increasingly relies on ultrasonic welding technology to improve production efficiency and product reliability, the semiconductor industry demands higher precision, speed, and data security from inspection technologies. Particularly in advanced packaging, any minor defect can lead to entire chip failure, posing a severe threat to product reliability. Therefore, how to efficiently and securely identify these rare defects on high-precision, fast-paced production lines has become a critical bottleneck limiting the yield improvement of advanced packaging.

Pain Points: Why This Hurdle Is So Difficult

In advanced packaging inspection, rare defect identification faces multiple challenges. Firstly, **data scarcity**: some critical defects occur extremely rarely, resulting in severely insufficient sample sizes for model training, making it difficult for traditional deep learning models to effectively learn their features due to “data starvation.” Secondly, **the balance between false positives and false negatives**: to avoid missed detections, traditional AOI systems often set thresholds too low, leading to normal process variations being misidentified as defects. Production line data from a leading manufacturer showed that the false positive rate for rare defect detection in traditional AOI once reached 15%, significantly increasing the burden of manual re-inspection, with re-inspection labor accounting for up to 30% of total inspection time. Thirdly, **the conflict between detection accuracy and cycle time**: advanced packaging production lines have fast cycle times, typically requiring millisecond-level inspection per chip. However, complex multi-layer structures and micron-level defects demand high-resolution imaging and complex image processing, making it challenging to balance detection accuracy with production line throughput. Lastly, and most critically, **data security and privacy**: semiconductor manufacturing processes are core competencies of enterprises. Any production data, especially defect image data, contains valuable process information, and its leakage could cause immeasurable losses. Therefore, customers have extremely high requirements for on-premises deployment of inspection solutions and keeping data within the factory.

The root cause of these difficulties lies in the complex advanced packaging processes, such as Fan-out, WLCSP, 2.5D/3D stacking, etc., where defect types are diverse and minute. In terms of imaging, defects under multi-layer structures may be obscured or present unclear edge features. Regarding materials, the significant differences in reflectivity and transmittance of various materials increase the difficulty of unified inspection. Combined with the current trend of ultrasonic welding applications in automotive components, which similarly emphasizes non-destructive and precise inspection, the limitations of traditional rule-based AOI in complex and variable scenarios are further highlighted, as is the urgent need for more intelligent and secure inspection solutions.

Technical Principles

DaoAI ACI OS operating system fundamentally solves the challenge of rare defect identification through its innovative Visual Foundation Model and APDT (Any-Positive-Defect-Training) few-shot learning technology. The system employs a self-supervised learning pre-trained visual foundation model, enabling it to learn general visual feature representations from vast amounts of unlabeled images, possessing powerful feature recognition capabilities. This means that even when encountering unseen rare defects, ACI OS can capture their subtle texture, shape, and structural anomalies. In practical applications, users only need to provide 1–20 good sample images, and ACI OS can complete 0-code automatic programming within 5 minutes, rapidly generating defect detection models, reducing changeover time from hours to minutes. For rare defects, APDT technology allows the model to quickly learn and generalize from an extremely small number (1-20) of positive (defect) samples, effectively overcoming the “overfitting” problem of traditional deep learning in few-shot scenarios. Furthermore, DaoAI ACI OS incorporates a semantic false positive filtering mechanism, which significantly reduces false positives caused by process fluctuations or background noise by combining contextual information and semantic features of defects. Actual test data shows that the system reduced a client's false positive rate by over 80%, greatly alleviating the burden of manual re-inspection.

Compared to traditional rule-based AOI or purely supervised learning methods, the advantage of DaoAI ACI OS lies in its adaptability and generalization capabilities. Traditional AOI requires manual setting of numerous thresholds and geometric rules; when faced with new defect types or process changes, it demands extensive time for reprogramming and debugging, and struggles to identify complex rare defects. Purely supervised learning, on the other hand, heavily relies on large-scale annotated data, rendering it almost ineffective for rare defect scenarios. The visual foundation model and few-shot learning mechanism of ACI OS enable it to generalize rapidly from limited experience (samples), much like human experts, quickly adapting to new inspection tasks. Crucially, its 100% on-premises private deployment ensures all production data remains within the enterprise, fully complying with the highest data security requirements of the semiconductor industry.

Typical Application Scenarios

• **Micro-crack Detection in Wafer-Level Packaging (WLP)**: Micro-cracks, often invisible to the naked eye, can occur during wafer dicing or grinding and may propagate under subsequent thermal stress, leading to failure. DaoAI ACI OS leverages the visual foundation model's sensitivity to subtle texture anomalies to precisely identify these micron-level rare cracks, even with very few samples, preventing potential reliability risks.
• **Voids or Insufficient Solder Joints in Flip Chips**: The quality of micro-bump soldering in flip chips directly impacts electrical connectivity and heat dissipation. Due to the extremely small size and vast number of solder joints, traditional inspection struggles with comprehensive coverage. ACI OS, with its high-precision feature recognition capabilities, effectively identifies rare defects such as tiny voids or malformed solder joints, ensuring connection reliability.
• **Foreign Object Contamination Detection in 2.5D/3D Stacked Packaging**: During multi-chip stacking, minute foreign objects like dust or fibers can easily be introduced, potentially causing short circuits or open circuits. As foreign objects vary widely in form and appear randomly, they are typical rare defects. DaoAI ACI OS's few-shot learning capability allows it to quickly learn their features from a small number of foreign object samples, achieving efficient and accurate online detection.
• **Minor Deformation or Detachment in Wire Bonding**: Occasionally, slight deformation, collapse, or localized detachment of bonding wires can occur during the wire bonding process, affecting signal transmission. ACI OS precisely analyzes the geometry and connection status of bonding wires, promptly detecting these subtle anomalies and enhancing the stability of bonding quality.

Case Study

A leading domestic semiconductor advanced packaging manufacturer, operating multiple high-precision packaging lines, faced challenges including high rare defect escape rates, escalating manual re-inspection costs, and data security risks for core process information. Before implementing DaoAI ACI OS operating system, the manufacturer's rare defect escape rate in a critical packaging process was approximately 2.5%, largely relying on manual secondary re-inspection to mitigate risks, which consumed a significant amount of labor (about 30% of total inspection time). Due to strict data security policies, the client explicitly required all inspection data to remain 100% within the factory, without being uploaded to any cloud. The DaoAI team, in response to the client's needs, adopted an ACI OS SDK/API on-premises private deployment solution, fully integrating the entire inspection system into the client's existing production line MES. All visual data, model training, and inference processes were completed on the client's own servers, ensuring data never left the factory. During deployment, DaoAI ACI OS used only 15 good sample images and 5 rare defect samples provided by the client, completing model training and deployment within 30 minutes. Post-implementation, production line data showed that the rare defect escape rate for this process was consistently controlled at <0.3%, representing a nearly 90% reduction compared to the traditional solution. Simultaneously, due to a significant drop in false positive rates, manual re-inspection volume decreased by 70%, greatly optimizing human resource allocation and reducing per-unit inspection costs by approximately 40%.

DaoAI ACI OS's on-premises private deployment solution not only solved our rare defect escape problem but also completely eliminated our data security concerns, truly keeping our core process data 'at home'.

DaoAI Solutions and Products

The core solution provided by DaoAI for rare defect detection in advanced packaging is our flagship product, the DaoAI ACI OS operating system. This system, through its core visual foundation model, bestows machine-like feature recognition capabilities, enabling rapid modeling for extremely rare defect types with only 1–20 defect samples via the APDT few-shot learning mechanism. For routine production line changeovers, DaoAI ACI OS supports 5-minute 0-code automatic programming with one good sample, greatly simplifying operations and enhancing production line flexibility. In terms of deployment, we adhere to a 100% on-premises private deployment principle, offering various integration methods such as SDK/API/Docker, ensuring that clients' core production data and intellectual property remain entirely within their enterprise firewall, with data never leaving the factory, thereby completely eliminating data security risks. Furthermore, the semantic false positive filtering function built into ACI OS effectively distinguishes between real defects and process noise, further improving detection accuracy and efficiency. DaoAI ACI OS is committed to providing an intelligent yet secure visual inspection platform, helping semiconductor clients achieve high yield, low cost, and high efficiency production goals. In scenarios requiring 3D morphological inspection, such as solder joint coplanarity or micron-level morphological defects, DaoAI 2D / 3D ACI equipment can complement by providing more comprehensive inspection capabilities through its self-developed 3D camera, but core decision-making and intelligent analysis are still driven by ACI OS.

Through the implementation of DaoAI ACI OS, this advanced packaging client achieved significant business value. In terms of data security, 100% on-premises private deployment completely mitigated data leakage risks, complying with the industry's most stringent regulatory requirements. Regarding production efficiency, the rare defect escape rate was reduced to <0.3%, significantly cutting down rework and scrap costs caused by defects flowing into downstream processes. Concurrently, the substantial reduction in manual re-inspection volume (decreased by 70%) directly translated into labor cost savings and overall production line efficiency improvements. ACI OS's rapid changeover capability (5-min 0-code programming) also enabled the production line to more flexibly respond to small-batch, multi-variety production demands, enhancing market responsiveness. These quantified achievements collectively establish DaoAI ACI OS's strong competitiveness in the advanced semiconductor packaging sector.

Full solution for this scenario: ACI OS industry solutions · Produce Ripeness Robotic Sorting: On-Premise Deployment

This article was generated by AI. Customer cases are simulated scenarios based on real product capabilities and figures are illustrative; see product pages for official benchmarks.