# ⚛  L1 Principle — Scanning Acoustic Microscopy (SAM)

**ID:** `L1-120` · **Status:** ⊙ Testnet (genesis catalog)

> **🌐 Domain:** Industrial Inspection — *High-frequency ultrasonic microscopy for buried interfaces*
> **🎯 Problem class:** linear inverse · **🧮 Solution space:** 3D acoustic impedance
> **📡 Carrier:** acoustic · **🌫 Noise:** gaussian
> **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41554212

---

## 🧠 1. Introduction

**Scanning Acoustic Microscopy (SAM)** is a **linear inverse problem** whose unknown lives in **3D acoustic impedance** space, within the **High-frequency ultrasonic microscopy for buried interfaces** sub-domain of **Industrial Inspection**.

Measurements consist of acoustic pressure waves recorded by transducers via a **scanning acoustic microscopy** sensing mechanism.

The forward operator applies, in order: L · acoustic lens operator; ordered pixel-by-pixel sampling; L · pulse echo operator; detector accumulates flux over the exposure window.

Observations are corrupted by additive Gaussian noise. Existence of the recovered 3D acoustic impedance is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by the declared priors. Stability is moderately conditioned (kappa_eff ~= 10); coupling_fluid_variation dominates the stability cliff; surface_curvature and the remaining mismatch parameters contribute higher-order bias terms. Additive gaussian thermal/electronic noise sets the irreducible data-fidelity floor, while mild Tikhonov or analytic inversion is sufficient at the nominal Omega point.

## ⚙ 2. Forward Model

Physical chain: **x** → L · acoustic lens → Raster scan → L · pulse echo → Temporal integration → **y** (detector).

```
y = ∫_t dt `L.pulse_echo` S_raster `L.acoustic_lens` x + n,    n ~ 𝒩(0, σ²)
```

**Measurement DAG:**

| Primitive | What it does |
|---|---|
| `L.acoustic_lens` | L · acoustic lens operator |
| `S.scan.raster` | Ordered pixel-by-pixel sampling |
| `L.pulse_echo` | L · pulse echo operator |
| `int.temporal` | Detector accumulates flux over the exposure window |

## 🔬 3. Physics Fingerprint

| Property | Value |
|---|---|
| Domain | Industrial Inspection |
| Sub domain | High-frequency ultrasonic microscopy for buried interfaces |
| Carrier | acoustic |
| Problem class | linear_inverse |
| Solution space | 3D_acoustic_impedance |
| Noise model | gaussian |
| Integration axis | temporal |
| Difficulty delta | 3 |
| L dag | 3.3 |

## 📡 4. Measurement Model

Existence of the recovered 3D acoustic impedance is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by the declared priors. Stability is moderately conditioned (kappa_eff ~= 10); coupling_fluid_variation dominates the stability cliff; surface_curvature and the remaining mismatch parameters contribute higher-order bias terms. Additive gaussian thermal/electronic noise sets the irreducible data-fidelity floor, while mild Tikhonov or analytic inversion is sufficient at the nominal Omega point.

| Metric | Value |
|---|---|
| Metric | PSNR_dB |
| Secondary | SSIM |

## 📏 5. Operating Range (Ω)

**Center problem class:** `sam` · **Forward operator:** `sam_forward`

**Center point:**

| Parameter | Unit | Value |
|---|---|---|
| H | px | 512 |
| W | px | 512 |
| Z | — | 64 |
| F mhz | MHz | 100 |
| Snr db | dB | 25 |
| Pixel um | µm | 10 |
| Surface curvature | — | 0 |
| Multiple reflections | — | 0 |
| Coupling fluid variation | — | 0 |

**Allowed bounds:**

| Parameter | Unit | Range |
|---|---|---|
| H | px | 64 – 2048 |
| W | px | 64 – 2048 |
| Z | — | 16 – 512 |
| F mhz | MHz | 10 – 2000 |
| Snr db | dB | 0.0 – 40.0 |
| Pixel um | µm | 0.5 – 500 |
| Surface curvature | — | 0.0 – 0.3 |
| Multiple reflections | — | 0.0 – 0.3 |
| Water path attenuation | — | 0.0 – 0.2 |
| Coupling fluid variation | — | 0.0 – 0.2 |

## 🎯 6. Tolerance (ε)

**Center tolerance:** 26.0

| Metric | Range |
|---|---|
| Psnr db | 5.0 – 40.0 |

## ⚖ 7. Hardness Function

Hardness scales as **`epsilon_fn`** on **PSNR_dB**, with κ = `200` and δ = `3`.

## 💾 8. Reference Dataset

- **primary** · weight 1.0 · IPFS _(not pinned yet)_

## 9. On-chain Registration

- **Chain hash:** `0xd3e211b87ec868c236c41128dc2020c1a2f0725d5c591d07f0f3260a5bd80a4e`
- **Chain tx hash:** `0x59bedcf1bb380e6c2178d52f606383d252ca9a8532db92b3b2e26da13489f085`
- **Chain block:** `41554212`

---

## File Mapping

This bundle consists of: `L1-120.md`, `L1-120.json`.

| File | Role | How to regenerate |
|------|------|-------------------|
| `L1-120.md` | Source of truth — edit this | Human or LLM |
| `L1-120.json` | Structured metadata for the registry | LLM regenerates from the sections above |

**Prompt for your LLM after editing this Markdown:**

> Read the attached Markdown. Regenerate the sibling `.json` so every field matches.
> Preserve the schema documented in the rows above.
> Output each file in its own fenced code block tagged with the filename.
> Output only the JSON object.

_This Markdown was auto-synthesized from the catalog row for `L1-120`._
_Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._