健康照護科技專題

2026年4月11日星期六

Placement & Rotuing

RF Power Amplifier

PA Placement

original, fixed (hardcoded), SA 1, QP, QP+SA (Small WireLen, non 0 viol.),

QP+SA with adaptive cooling schedule (2.55 WireLen, usual. 0 viol.)
add c
ongestion penalty in energy (track overflow significantly reduced, Overflow Comp, Stats)

QP+ILP+PrePass (No A* or SA, viol.) comparison with ACS summary, detail

Slides

BJT Differential Pair

schematic

IC Die — Parameter Optimization by SA

artifact

Quadratic Placement

ILP+ PathFinder +A* Routing

(share)

Plus Multi-layer Router (95% reduction of congestion md)

Plus Symmetry Hard Constraint (cong=0 match>97%, artifact)

neat lohas style

2026年4月8日星期三

健康照護科技專題(二) Syllabus 2026 Fall

(切換完整版課綱)

開場

Unit 1: AI Computer Vision

CNN (Convolutional Neural Networks) Handwashing

YOLO (You Only Look Once) YOLOv7 list

ViT (Vision Transformers) wafer defects heat map comparison

VLM (Vision Language Models) nutrition analysis

Tesla AI Vision

HW#1 AI in Computer Vision

Unit 2: 盲人環境輔助理解，空間導航

空間資訊與識別輔助

盲人導航影像辨識 (基礎)

真實世界 3d 模型製作

3d 導航(why 3D)

HW#2 Understanding environments

Unit 3: 3d障礙偵測演算法

3d障礙全域分析

障礙偵測(Recast 原理)

Simulating A Navigation Assistant with sonar

HW#3 Obstacle detection

Unit 4: 避障演算法

Voronoi an introduction, Voronoi for Place Names

Voronoi 2D navigation (show Voronoi) (Rural Road 2d)

Voronoi-based Safe Routing Planning (Country Alley 2d)

Voronoi waypoints for navigation (share) (Campus Alley 3d) vs. non Voronoi

3D Voronoi障礙偵測 with A*

A* search for Voronoi Navigation around moving obstacles

Unit 5: 環境安全檢測與評估視覺化 using Claude

Hazard detection (環境危害偵測)

意外事件預測與模擬

Home Hazard chain identification and animation （居家危害鏈偵測與危害視覺化）

Unit 6:

Unit 7: AI for Game Designing

Games

Unit 8 Space-Time Evolution

Geological evolution of Taiwan

Unit 9 Advanced Prompts

How to scale up: Compound Artifacts

如何合成複雜創作

Unit 10

AI for Academic Documentation

__________________________________________________________________

規模 9.0 程式設計的地震

Anthropology

Human migration

Design

Biology

Cat coat colors: 貓的花色遺傳模擬
生物呼吸中的量子效應互動模擬

6/2 AI for naturalistic modeling

Fractals

___________________________________________________________

Prompt Engineering Guide

Japan in Geological evolution

Document Editing Prompt

Prompts for scientific accuracy

Scientific animation
Financial Analysis in AI

2026年4月7日星期二

HW#6 A* Circuit Optimization

本次作業詳細說明

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#6 ［your id, your name]

任選一題

1. Design a Two-Stage BJT Amplifier according to goals specified using A* (Parameters to optimize RE1, RC1. RE2. RC2)

A* search for optimization, share (c_mu=3 pF, c_pi=22 pF)

2. Optimize LM3886 class AB audio amplifier IC using A* (Parameters to optimize: resistors except the load)

LM3886

A Search Setup*

State space: 5 components × 5 E12 values each = 3,125 possible states
g(n): actual steps taken from the start configuration
h(n): circuit performance cost (the heuristic) — sum of 5 weighted penalties
f(n) = g + h: A* priority queue ordering

Cost function (5 components, all minimized):

Gain: quadratic penalty for deviation from 26 dB (20×)
f_low: penalty if low-frequency cutoff exceeds 10 Hz
f_high: penalty if high-frequency cutoff falls below 100 kHz
Bias: penalizes Rb1 ≠ Rb2 (asymmetric thermal tracking)
Re stability: log-scale penalty away from 0.47 Ω optimum

Weight/ Forms

Are Re1 and Re2 Always Equal?

the architecture inside the LM3886 and TDA7293

optimizer A* (share)

Lessons learned from AI use

Lesson 1

Design a Two-Stage BJT Amplifier (Parameters to optimizeRE1, RC1. RE2. RC2)

Note this is a degenerate model

A* search for optimization, share 4-parameter (for bypassed model c_mu=3 pF, c_pi=22 pF) note: goal reached is actually current step, not current best

revised to OCT model A* search for optimization, share 4-parameter (for bypassed model c_mu=3 pF, c_pi=22 pF)

using 2N2222 parameters (share)

Lesson 2

Lesson 3

(a) Degenerative/NonDegenrative ＯＣＴ Global Exhaustive Search/A*

(b) plus MNA-based Freq sweep

2026年3月31日星期二

HW#5 LNA Optimization

1, 2 任選一題

本次作業詳細說明

1. Make a GD/(A*)+MNA Optimizer on a Apple C1 LNA Die

Hint: artifact, share

DE+MNA Optimizer on Die artifact

2. 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)