> I am designing a Python-based educational simulation to demonstrate Statistical Process Control (SPC) in an integrated circuit (IC) testing environment.

Please conduct a deep technical research review with the following scope:

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1. Industry Context
- What are the common manufacturing processes and failure modes in IC production and testing?  
  (e.g., open/short faults, bonding issues, probe misalignment)
- Where is SPC most commonly applied in the semiconductor manufacturing pipeline?
- What are typical KPIs for quality control in IC testing?

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2. SPC Techniques
- Describe key SPC control charts (e.g., P-chart, X-bar, C-chart) suitable for binary pass/fail or measurement data
- How are control limits defined (e.g., 3σ rule)?
- What are early warning signs of process drift, and how are they detected using SPC?

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3. Trend Detection & Fault Isolation
- What trend analysis techniques are used to detect systematic process drift (e.g., linear increase in failure)?
- How is SPC combined with Root Cause Analysis (RCA) in real manufacturing?
- Describe how real-time data is used to trigger maintenance or human inspection

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4. AI + SPC Integration
- How are AI/ML or LLMs being used today to:
  - Interpret SPC charts?
  - Summarize test station logs?
  - Recommend maintenance actions?

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5. Academic and Industrial References
- Cite recent publications or real-world case studies of SPC in semiconductor manufacturing or smart factory applications  
- Include any open-source tools or datasets relevant to IC testing SPC