🧱 Physical-First Engineering

🧠 Intelligent Control Architecture

An Integrated Design Philosophy for

Semiconductors / Control / AI

🔗 https://samizo-aitl.github.io/

❓ Why Physical-First Engineering Matters

  • 🤖 AI and control systems are becoming black boxes
  • 📐 Mathematics, models, and implementation are fragmented
  • 🧩 Semiconductors, control, and AI are discussed separately

👉 Engineering systems require
causal and hierarchical structures

🏗 Hierarchical Structure of Engineering Systems (Concept)

  • 🧱 Physics (Materials / Devices)
    Material properties, structural constraints, real behavior
  • 📐 Models (Mathematical / Equivalent Circuits)
    Meaningful parameterization
  • 🎛 Control (PID / FSM)
    Real-time stability and deterministic control
  • 🧠 Intelligence (LLM)
    Diagnosis, decision-making, and redesign (supervisory layer)

👉 Each layer has
clearly separated responsibilities, time scales, and roles

🧭 Role Allocation by Layer

  • 🧱 Physics
    Material properties, device behavior, structural constraints
  • 📐 Models
    Mathematical models, equivalent circuits, parameterization
  • 🎛 Control
    Real-time stability via PID and FSM
  • 🧠 Intelligence
    Diagnosis, decision-making, and redesign using LLMs

👉 “Intelligence” belongs only to the outermost layer

🧪 Implementation in Samizo-AITL

  • 🧱 Edusemi-v4x
    Physics, device models, semiconductor processes
  • 🎛 EduController
    Control design using PID and FSM
  • 🧠 AITL Framework
    Supervisory and redesign layer powered by LLMs

👉 Each material represents
a different layer extracted from the same structure

🧱 Physics & Models Layer

  • Material properties and device physics
  • Compact models / equivalent circuits
  • Parameters with physical meaning

❌ Black-box approximation
❌ Direct AI-based fitting

👉 Models without meaning are not acceptable

🎛 Control Layer (PID & FSM)

  • Real-time feedback control using PID
  • State and mode management using FSM
  • Deterministic and explainable behavior

❌ Probabilistic decision-making in real-time loops

👉 Stability is guaranteed by
mathematics and structure

🧠 Intelligence Layer (LLM)

  • Decision-making as a supervisory layer
  • Fault analysis and performance diagnosis
  • Controller redesign and parameter tuning

👉 Operates outside the real-time control loop
👉 Extends human thinking, not replaces it

🔁 AITL: Closed-Loop Intelligent Control Architecture (Concept)

  • 🧱 Physics / Models
    Define system states and models
  • 🎛 Control (PID / FSM)
    Drive physics via control inputs
    Send states and logs upward
  • 🧠 LLM (Supervisor)
    Diagnosis, redesign, and adjustment commands
    (Outside the control loop)

👉 Human insight × Machine reliability

🧪 Implementation & Proof of Concept

  • 🧩 V–I controlled ASIC (SKY130)
  • 🔢 Fixed-point PID / FSM implementation
  • 🧮 Design verification using SystemDK × FEM

👉 From physical models
👉 All the way to silicon implementation

🎓 Value Provided by Samizo-AITL

  • 🧱 Physics-driven and explainable engineering design
  • 🎛 Deterministic control with intelligent supervision
  • 📘 Reproducible engineering education from models to implementation

🔗 https://samizo-aitl.github.io/

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