ePlace vs. RePlAce
星期六, 4月 25, 2026
modern global placement: ePlace vs. RePlAce
Two-stage Cascode Class-AB RF power amplifier: parameter optimization
Two-stage Cascode Class-AB RF power amplifier targeting 5G n78 (3.5 GHz) in TSMC 28nm RF
星期二, 4月 21, 2026
Two-stage Cascode Class-AB RF power amplifier: Placer
original , fixed (hardcoded), SA 1, QP, QP+SA (MNA, Small WireLen, non 0 viol.),
- QP+SA with adaptive cooling schedule (2.55 WireLen, usual. 0 viol.)
- add congestion penalty in energy (track overflow significantly reduced, Bench, Overflow Comp, viz, detail viz)
QP+ILP+PrePass (No A* or SA, viol.) comparison with ACS detail
Slides fit of SA for RF PA Layout
Two-stage Cascode Class-AB RF power amplifier targeting 5G n78 (3.5 GHz) in TSMC 28nm RF
RF Power Amp Driver TSMC N16FFC-RF
RF Power Amp Driver TSMC N16FFC-RF (one stage)
(Apple C1 by TSMA N4P is more advanced, no longer apply, not even N3E)
PA Lessons, PA Verified by computing the exact KCL residual at its converged solution, not because of blow up of MNA
RF PA driver with 10 dBm output, not PA itself (realistic spec, not just renaming)
TSMC N16FFC-RF revision (under construction)
PA Driver, Real C1 transceiver RF blocks on N4P
星期三, 4月 15, 2026
EX#7 Mathematical Optimization
Assignment Structure 作業架構
md version
Deadline: Next Saturday at 23:59 (one more week) 期中考緣故,順延一周
Send all the share links to me chang212@gmail.com by email with subject EX#7 [your id, your name]
Students choose from three progressively harder assignments. Each builds on the interactive "Optimization in Hyperspace" 3D visualizer, which demonstrates five algorithms navigating a fitness landscape.
同學從三份難度遞增的作業中逐一完成。每份皆以互動式「超空間最佳化」3D 視覺化工具為基礎,展示五種演算法在適應度地形上的行為。
Problem 1 — Conceptual Understanding 概念理解
- Task 任務: Written reflection (300–500 words) on how each algorithm behaves on the landscape — local vs. global optima, exploration vs. exploitation.
- 撰寫心得(300–500字),描述各演算法在地形上的行為差異——區域最佳解 vs. 全域最佳解、探索 vs. 開發的取捨。
- Deliverable 繳交物: md (1–2 pages)
- Difficulty 難度: ★☆☆
Problem 2 — Mathematical Analysis 數學分析
- Task 任務: Implement gradient descent from scratch on a multimodal 2D function f(x,y) = sin(x)·cos(y) + 0.1(x² + y²). Test 3 starting points × 3 learning rates. Plot the surface + convergence paths.
- 自行實作梯度下降法,應用於多峰 2D 函數。測試 3 個起點 × 3 種學習率 (α = 0.01, 0.1, 0.5),繪製曲面與收斂路徑。
- Deliverable 繳交物: Code + 1-page report in artifact
- Difficulty 難度: ★★☆
Problem 3 — Comparative Algorithm Design 演算法比較實驗
- Task 任務: Compare Gradient Descent vs. Simulated Annealing (exponential cooling T(t) = T₀·γᵗ). Run 50 trials each from random starts; vary T₀ and γ; produce success-rate table + plot.
- 比較梯度下降 vs. 模擬退火法(指數冷卻排程)。各執行 50 次隨機起點實驗,調整 T₀ 與 γ,製作成功率表格與圖表。
- Deliverable 繳交物: Code + 1–2 page analysis with table & plot in artifact
- Difficulty 難度: ★★★
Five Algorithms in the Visualizer 視覺化工具中的五種演算法
Algorithm 演算法 Type 類型 Key Trait 特性 Gradient Descent 梯度下降 Local 區域 Follows steepest slope; gets trapped in local optima 沿最陡方向走,易陷入區域最佳解 Nelder-Mead 單純形法 Local 區域 Derivative-free simplex; can stall near local optima 無需導數,但仍可能停在區域解 A* Search A*搜尋 Heuristic 啟發式 Graph-based, uses heuristic to guide search 圖形搜尋,以啟發函數引導 Simulated Annealing 模擬退火 Global 全域 Accepts worse moves probabilistically; escapes traps 以機率接受較差解,可跳脫陷阱 Global Optimization 全域最佳化 Global 全域 Systematic global search 系統性全域搜尋
| Algorithm 演算法 | Type 類型 | Key Trait 特性 |
|---|---|---|
| Gradient Descent 梯度下降 | Local 區域 | Follows steepest slope; gets trapped in local optima 沿最陡方向走,易陷入區域最佳解 |
| Nelder-Mead 單純形法 | Local 區域 | Derivative-free simplex; can stall near local optima 無需導數,但仍可能停在區域解 |
| A* Search A*搜尋 | Heuristic 啟發式 | Graph-based, uses heuristic to guide search 圖形搜尋,以啟發函數引導 |
| Simulated Annealing 模擬退火 | Global 全域 | Accepts worse moves probabilistically; escapes traps 以機率接受較差解,可跳脫陷阱 |
| Global Optimization 全域最佳化 | Global 全域 | Systematic global search 系統性全域搜尋 |
Grading Notes 評分備註
- problem 1 is reflection-based — check for genuine engagement with the visualizer, not just generic definitions. HW1 為心得型——確認同學確實操作過視覺化工具,而非僅抄定義。
- problem 2, 3 require working code — verify plots are generated from their own implementation, not copied. HW2/HW3 需繳交可執行程式碼——確認圖表由自己實作產生。
- problem 3 requires statistical rigor (50 runs, multiple parameter settings). Check for proper experimental design. HW3 需具備統計嚴謹度(50次試驗、多組參數),檢查實驗設計是否完整。
RF PA IC Design Sub 6 GHz
original , fixed (hardcoded), SA 1, QP, QP+SA (MNA, Small WireLen, non 0 viol.),
- QP+SA with adaptive cooling schedule (2.55 WireLen, usual. 0 viol.)
- add congestion penalty in energy (track overflow significantly reduced, Bench, Overflow Comp, viz, detail viz)
QP+ILP+PrePass (No A* or SA, viol.) comparison with ACS detail
Slides fit of SA for RF PA Layout
Two-stage Cascode Class-AB RF power amplifier targeting 5G n78 (3.5 GHz) in TSMC 28nm RF
RF Power Amp Driver TSMC N16FFC-RF (one stage)
(Apple C1 by TSMA N4P is more advanced, no longer apply, not even N3E)
PA Lessons, PA Verified by computing the exact KCL residual at its converged solution, not because of blow up of MNA
RF PA driver with 10 dBm output, not PA itself (realistic spec, not just renaming)
TSMC N16FFC-RF revision (under construction)
TSMC N4P























