Monday, November 03, 2025

ASML

ASML High-NA EUV Lithography: The Mechanical Marvel Atomic-precision motion control + 40 years of mechanical engineering mastery 🏭 CLASS 1 CLEANROOM - Everything inside costs $$$$ EUV Source (LPP) CO₂ Tin droplets → Plasma @ 500,000K → 13.5nm EUV Collector Mirror M1 M2 Complex Mirror Optics 6-14 mirrors, each with 40+ layers (0.1nm precision) Zeiss exclusive, $100M+ per set Reticle Stage (4× pattern) Reticle Mask Pattern Synchronized Stage (4:1 ratio) Projection Optics (0.55 NA) 4× demagnification ⚙️ WAFER STAGE: The Real Moat (40 Years of R&D) 300mm Wafer 6-DOF Motion Control x, y, z, Rx, Ry, Rz - all synchronized 4G acceleration @ atomic precision < 0.1nm positioning < 2nm overlay Laser Interferometers X-axis position Y-axis position Z-axis position Real-Time Control 1000+ corrections/sec Temp: 0.001°C Vib. isolation SYNC ASML's 40-Year Moat: Key Technologies 1. Motion Control ✓ Air bearing stages ✓ Magnetic levitation ✓ Voice coil actuators ✓ Piezo fine positioning ✓ 6-DOF simultaneous Dev time: 15+ years Japanese precision + Dutch integration Nikon/Canon couldn't replicate 2. Metrology ✓ Laser interferometers ✓ Encoder systems ✓ Alignment sensors ✓ Real-time correction ✓ Sub-nm accuracy 1000+ measurements/sec Proprietary algorithms Hardware + software IP 3. Synchronization ✓ Reticle + Wafer sync ✓ 4:1 speed ratio ✓ Overlay < 2nm ✓ Dynamic focus ✓ Vibration damping 4G acceleration sync'd Dual-stage architecture 200 wafers/hour throughput 4. Supply Chain ✓ Zeiss mirrors (exclusive) ✓ Japanese bearings ✓ US control systems ✓ Dutch integration ✓ 1000+ suppliers Ecosystem depth 40 years of relationships Cannot be replicated quickly This is why Intel with $100B+ budget still can't catch up. This is why Substrate's "solve the light source" strategy misses 99% of the challenge.

Substrate

Substrate XRL Mechanism (Animated) Watch: Electrons → X-rays → Pattern Transfer (with motion control challenge) 💰 CHEAP LAND (Outside Cleanroom) Particle Accelerator (Synchrotron / Linear) X Target ⚡ Electron Acceleration • Energy: ~GeV scale • Beam current: mA range • Pulse rate: MHz • X-ray flux: High ✓ Large size OK - no cleanroom needed here Cost savings: Build on cheap industrial land ~$40M per tool (estimated) X-rays (~1nm) Vacuum beam transport 🏭 EXPENSIVE CLEANROOM XRL Exposure System X-ray Mask ↕ Proximity gap (~10-50μm) Silicon Wafer + Photoresist ⚙️ Precision Wafer Stage 6-DOF motion control required ±0.1nm needed! ⚠️ THE CHALLENGE Atomic-level precision motion control: • <0.1nm positioning • Real-time correction • Overlay accuracy Process Flow: How XRL Works 1️⃣ Accelerate Electrons accelerated to GeV energies in particle accelerator ✓ Solved (National lab tech) 2️⃣ Generate e⁻ hits target → X-rays produced via Bremsstrahlung ✓ Solved (Physics understood) 3️⃣ Transport X-rays travel through vacuum tube to lithography chamber ✓ Solved (Standard engineering) 4️⃣ Pattern X-rays pass through mask, expose wafer (proximity printing) ⚠️ Partial (Alignment needed) 5️⃣ Control Nanometer motion control & alignment across full wafer ❌ UNSOLVED (The moat!) Animation shows: electrons → X-rays → patterned exposure → moving wafer stage (emphasizing control challenge)

Substrate vs. ASML

Substrate 的致命盲點:被忽略的護城河 光源只是冰山一角,奈米級運動控制才是真正的地獄 ✓ Substrate 聲稱已解決 💡 X 光源亮度 • 國家實驗室 30 年技術 • 粒子加速器商業化 💰 成本優勢 • 4000萬 vs 4億美元 • 加速器建在無塵室外 ✗ ASML 真正的護城河(未解決) ⚙️ 奈米級運動控制系統 • 晶圓台定位精度 < 0.1 nm(原子級) • 4G 加速度 + 即時校正(每秒千次) • 6 自由度同步控制 + 溫控 0.001°C 🎯 對準系統(Alignment) • Overlay 精度 < 2nm • X 光穿透力強 → 無法用光學掃描標記 🏭 供應鏈生態系 • Zeiss 反射鏡(40 層膜厚 0.1nm 精度) • 日本氣浮台、雷射干涉儀 • 40 年機械工程 know-how 📊 良率與缺陷管理 • 實驗室單次曝光 → 每小時 200 片晶圓 • 良率從 0% 爬升到 99% 的死亡谷 技術難度評估 X 光源 光學系統 運動控制 供應鏈 良率管理 ⭐⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ ⚠️ 從實驗室到量產的鴻溝 國家實驗室環境 ✓ 靜態樣品 ✓ 單次曝光 ✓ 手動精密調整 ✓ 時間無限 ✓ 良率無要求 ? 量產製造環境 ✗ 高速連續運動 ✗ 每小時 200 片 ✗ 全自動對準 ✗ 良率 > 95% ✗ 成本可控 📚 歷史教訓:為何光學巨頭會輸? 🇯🇵 Nikon 的失敗 有光源、有光學專長 但系統整合能力不足 📰 Canon 的奈米壓印 理論可行、成本更低 但 overlay 控制失敗(2024 仍未量產) 💵 Intel 的數百億美元 有資金、有人才、有供應鏈 EUV 時代仍追不上台積電 🎯 核心結論 Substrate 的論述是「物理學家的樂觀」,忽略了「機械工程師的現實」 半導體製造 = 物理 × 化學 × 機械 × 材料 的四維挑戰 解決一個維度,距離成功還有 99%

Tuesday, October 28, 2025

A* macro thinking for electronic engineering problems







A* macro thinking framework

A* macro

Framework  (circuit analysis search tree)

Visualization 


2-stage amp




Bode plot visualization

Colab


CE  (Small-signal transistor gain analysis)




 

HW#8 A* macro thinking Part 3 (Backtracking)

 課堂練習 

Deadline: Regularly This Saturday at 23:59 因期中考緣故繳交日延後一周(11/10/2025)

Send all the links to  me chang212@gmail.com by email with subject HW#8   [your id, your name].


1. 使用聰明狗 A*模胚,模擬聰明狗 A* 每個情境的動畫




2. 續上題,依據決策樹自動模擬每個情境



3. 提升美學與完成度,進入v1從這裡開始






4. Solve for the gain.  Then Frequency response. 


5. Solve for the gain. Then frequency response











參考解答


Gemini 2.5 Pro for 2-stage amp, Freq Response





 以上參考答案
(4) -4 (5) 8513  答案誤差10%以內都是正常的





Backtrack

  1. Application of Reed-Solomon codes
  2. Hamming code
  3. Reed-Solomon codes
  4. Computer Network
  5. Deep Learning Neural Networks
  6. A* search


Build A* tree synced with animation 







portrait (Run A*), node manually interactive






Advanced visualization with backtrack included and transit animated (Data visualization techniques) 










Tuesday, October 21, 2025

HW#7 Complex Constraints (Synchronization)

建議工具

使用 Claude Sonnet 4 推理模式(手動切換,免費用戶額定時間內只能使用三次)

使用 ChatGPT 5 推理模式(自動切換)

使用 Gemini 2.5 Pro 免費額度最高 1M tokens (永遠推理模式)

使用 Grok 4 推理模式(自動切換)


課堂練習 

Deadline: This Saturday at 23:59

Send all the share links to  me chang212@gmail.com by email with subject HW#7 [your id, your name]


任選兩題


1. 推理模式 special topic Smart Dog Ball Retrieval 











2. 搶救感恩節晚餐大作戰講義 題目 結果視覺化


至少做兩種工程圖


 狀態圖(State Diagram) 










state diagram with aligned timeline





Build Location centered animation




看板圖 (Kanban)


 (interactive timeline)




流程圖(Flow chart)







3. Dinner Operation A* macro thinking


Build A* search animation







 




Advanced visualization with backtrack included and transit animated (Data visualization techniques) 

note:  Under A* search heuristics,  backtracking does not happen in this case. Therefore, "backtracking" here is a borrowed concept. Tree may not be accurate as Sarah is left out on the goal path.