Semiconductor manufacturing, as a knowledge-intensive industry, often faces a tricky challenge: the inheritance of intangible 'knowledge'. A common phenomenon is that senior engineers retire, taking away decades of equipment operation experience, new employees are overwhelmed by piles of equipment manuals, and key fault handling solutions are scattered in PPT reports across different departments. Knowledge has become the most valuable asset for semiconductor companies, but due to fragmentation and isolation, it cannot fully realize its value. Knowledge silos and experience gaps are quietly affecting factory operational efficiency and innovation capabilities.

The Knowledge Base Agent—ZhiJiang launched by GETECH is a profound response to this core pain point. ZhiJiang is an enterprise-level knowledge management hub that integrates a 'dual-engine' architecture, intelligent parsing, and multimodal interaction, dedicated to fundamentally transforming scattered knowledge fragments from 'fragmented' to 'systematic' through AI technology.

Addressing the Three Major Challenges: Knowledge Fragmentation, Isolation, and Inheritance Difficulty

Semiconductor factory knowledge management faces three challenges.

Knowledge Fragmentation: Massive unstructured documents such as equipment manuals, SOPs, fault reports, case summaries are scattered across departments with varying formats, forming information silos.

Experience Inheritance Difficulty: The tacit knowledge of senior experts is hard to effectively accumulate, and high-frequency talent turnover and long training cycles make 'experience gaps' the norm.

Inefficient Knowledge Access: Engineers need to spend a lot of time manually searching, fault handling response is slow, affecting the continuous and stable operation of the production line.

'Vector + Graph' Dual Database Drive, 'Retrieval + Reasoning' Accuracy Doubled

ZhiJiang's core breakthrough lies in building a knowledge base driven by a 'vector database + graph database' dual-engine. The vector database, with its powerful semantic understanding capability, enables 'intelligent retrieval' of knowledge, accurately capturing intent and finding relevant information even with colloquial or incomplete queries. The graph database constructs an association network between equipment, faults, and process parameters, endowing the system with 'logical reasoning' ability to trace problem roots and recommend related solutions.

The 'vector database + graph database' dual-engine collaboration retains semantic-based retrieval power while enhancing knowledge reasoning. When an engineer asks about 'etching machine chamber pressure fluctuations', the system not only provides tuning guides but also associates historical similar cases, key parameters affecting yield, and even recommends preventive maintenance measures, forming a decision-making loop.

Multi-Scenario Empowerment, from Equipment Maintenance to New Employee Training

ZhiJiang's value is fully demonstrated in various semiconductor manufacturing scenarios.

In the MFG scenario, employees can use ZhiJiang to monitor production processes, equipment status, and process parameters in real-time. It integrates multi-source heterogeneous data (e.g., SOPs, reports) to improve production transparency and decision efficiency through structured analysis.

In the equipment maintenance scenario, engineers can use AR glasses to see the equipment structure, obtain disassembly guidance via voice, view 3D models, and reduce mean time to repair by 30%.

In daily operations and training, employees can ask questions anytime via natural language, quickly obtain SOPs and alarm handling guides, improving knowledge retrieval efficiency by 90% and significantly shortening new employee training cycles.

In a semiconductor customer's practice, ZhiJiang successfully parsed years of accumulated engineering reports and case summaries, building a systematic knowledge network covering equipment knowledge, process experience, and fault handling. Engineers can obtain precise solutions simply by asking in natural language, significantly improving problem-solving efficiency. This proves its capability in handling semiconductor professional knowledge.

Notably, the ZhiJiang Agent is not isolated but deeply integrated into GETECH's CIM AI Foundation framework. It collaborates with the Octopus AI Agent platform to build specialized Agents focused on equipment knowledge management; links with GT Insights to provide process knowledge support for yield analysis; and combines with AI FDC to offer knowledge basis for fault prediction and maintenance. ZhiJiang is not just a knowledge retrieval tool but becomes the 'knowledge brain' of the entire smart manufacturing system. It enables knowledge to be proactively pushed to the right people at the right time and in the right way, achieving a shift from 'people finding knowledge' to 'knowledge finding people'.

The implementation of GETECH's ZhiJiang marks the evolution of semiconductor knowledge management from a passive 'document warehouse' to an active 'knowledge empowerment platform'. In the semiconductor industry where knowledge density determines industrial height, ZhiJiang is building a continuously appreciating, inexhaustible knowledge asset treasure trove for enterprises.