HW#9

  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#9 [your id, your name]

1. Benchmark the three algorithms (GD, NM, A*) and their cascades (one designed by you and the other design by AI )

• For the five methods, each runs with 10 seeds
• Tabulate Gain, Pout, PAE, OP1dB, S₁₁ (5 methods = 5 tables, each table with 5 metrics × 10 seeds = 40 cells).
• Visualize all of your data

Two-stage Cascode Class-AB RF PA Driver targeting 5G n78 (3.5 GHz) in TSMC 28nm RF

Build Sub 6 GHz Power Amplifier Optimizer with Die Synced

share, artifact (Closed-Form)

2. Benchmark the three designs of RF PA Drivers

2-stage RF PA driver 

Closed-form Optimizer with Load-Pull Contours 

Performance-first Thermal build  ∠Γ_3f₀ phase (deg) · open=0, -170 deg

Margin-first Thermal build ∠Γ_3f₀ phase (deg) · open=0, -170 deg

Using Cowork or Claude.ai, benchmark the three models. Experiment with 5 seeds. For each seed, optimize the circuit design using built-in A* and then NM. 

• For each seed, record metrics from the cascaded optimizers: 
• Compare Closed-Form vs. margin-first build vs. comprehensive model. 
• -Tabulate Gain, Pout, PAE, OP1dB, S₁₁ (5 metrics × 10 seeds = 40 cells).
• -Identify which metric the closed-form most over- or under-estimates.
• Cross-check both thermal designs: compute junction temperature rise ΔT_j at peak Pout. Which design runs cooler? By how many °C?
• -Build a 2×2 comparison table: rows = {Performance-first, Margin-first}, columns = {Fitness, min-spec-margin (dB), ΔT_j (°C), PAE @ 6dB back-off}.
• Visualize all of your data

Supplemental

Margin vs. Performance builds

design choices