- Expertise: 3 years in Semiconductor Equipment (Commissioning/Startup).
- Core Skill: Translating hardware physical logic into predictive Python models.
- The "10-Day Sprint": In just 10 days at Kaggle, I digitized my 3-year EE experience into high-impact RCA simulations.
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Chemical-Contamination-Analysis-And-Predictive-Modeling
- View Technical Deep Dive ➔
- Exposing the 98.6% Risk Gap through dynamic
$T_{lag}$ modeling. 
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- Quantitative modeling of chemical leak paths based on hardware expertise.
- 核心專長：3 年半導體設備實務（裝機/調試），專精於系統性根因分析 (RCA)。
- 核心價值：具備將硬體物理邏輯轉化為 Python 預測模型的能力。
- 10 天學習展示：我利用 10 天時間學習Kaggle數據工具，將過去 3 年累積的設備直覺數位化。
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Chemical-Contamination-Analysis-And-Predictive-Modeling
- 查看詳細技術解析 ➔
- 透過動態流體延遲建模，揭露傳統 SOP 中隱藏的 98.6% 風險漏洞。
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- 結合裝機經驗，針對 5nm 製程化學品洩漏路徑進行定量模擬與分析。
📫 Connect with me: [Linkdin正在拿回登入權限中] | [app258369@gmail.com]