HW#4 RF IC Design

  

建議工具

使用 Claude Sonnet 4.6 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 3.0 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)

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How to publish a Claude artifact

How to share a ChatGPT link

How to share a Grok link

How to share Gemini Link

Content share 作業繳交格式

• share only link, pure text, markdown (md)
• no attachments accepted, no html, screen dump, or png
• non-compliant homework will be rejected and returned to you

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#4  [your id, your name]

3/10 只要做第一題（內有5小題）

Study Apple C1 Architecture

1. (a) On Claude, Run NGSpice for the LNA using given parameters. If the results do not match, explain why.

(b) Run an MNA simulation (Modified Nodal Analysis, as used in Cadence Spectre engine)

 

Optimized parameters (share)

MNA simulation

(c)  Perform DRC (Design Rule Check) for the  3.5 GHz LNA. If it is not entirely design rule compliant, redesign to make the layout more DRC driven.

(DRC, share) (DRC-driven, artifact)

2. (a) Run the Simplified LNA optimizer on Die, 3.5 GHz LNA TSMC N7. Use MNA (Modified Nodal Analysis, as used in Cadence Spectre engine)  to optimize again. Must verify your results to meet spec and parameters have to be realistic.

Hint: (GD/(A*)+MNA Optimizer on Die artifact, share)

DE+MNA Optimizer on Die artifact

(b) To achieve the 15-20 dB gain target and improve the LNA performance to match the schematic specs, use Differential Evolution Optimization to optimize the LNA performance.

Must verify your results to meet spec and parameters have to be realistic.

Hint: (DE+MNA Optimizer on Die artifact) 

3. (a) Build a 9.0 GHz Fractional-N PLL Synthesizer: 9.0 GHz PLL with A* Optimization, (share) 65 nm

(b) Build a 9.0 GHz Fractional-N PLL Synthesizer with NM Optimization

(c) Build Apple C1 ADPLL — 7.0 GHz (spec) with A* optimization, TSMC N7

Apple C1 ADPLL — 7.0 GHz  

(d) Build Apple C1 ADPLL with NM Optimization

Hint: What is NM method? NM Algo

EX#4 Search and Visualizaiion

  Handouts

Chain of Thought with Example, Make your AI powerful

Constraints,  Visualization on energy landscape

Search Algorithms: A*, BFS, Dijkstra

River Puzzles  (Pz 2, Pz 20) by Chain of Thought vs. Search Algortihms

BFS/A* Scalability: 8 vs. 20 persons

  課堂練習 

Deadline: This Saturday at 23:59

Send all the share links to  me chang212@gmail.com by email with subject EX#4  [your id, your name]

1.   

Solve Pz 2  by A*/BFS

Animate search tree with synced progression (A*/BFS side-by-side)

 2. 

Solve Pz 20 by  A*/BFS 

Animate search tree with synced progression (A*/BFS side-by-side)

3.  Solve Dog Ball Retrieval by A*/BFS 

Animate search tree with synced progression (A*/BFS side-by-side)

EX#3

 

  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#3  [your id, your name]

1.   

Solve Pz 2 using chain of thought (CoT) reasoning with each step whether correct or incorrect, showing backtracking. 

Animate the CoT process in the above synced with river crossing scenarios.  

Animate search tree with synced progression (A*/BFS side-by-side)

 2. 

Solve Pz 20 using chain of thought (CoT) reasoning with each step whether correct or incorrect, showing backtracking. 

Animate the CoT process in the above synced with river crossing scenarios.  

Animate search tree with synced progression (A*/BFS side-by-side)