64 lines
3.6 KiB
Markdown
64 lines
3.6 KiB
Markdown
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# Implementation Plan
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This plan outlines the step-by-step implementation of the `inire` auto-router. For detailed test cases, refer to [Testing Plan](./testing_plan.md).
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## Phase 1: Core Geometry & Move Generation
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**Goal:** Implement Ports, Polygons, and Component Library with high geometric fidelity.
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1. **Project Setup:** Initialize `inire/` structure and `pytest` configuration. Include `hypothesis` for property-based testing.
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2. **`geometry.primitives`:**
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* `Port` with **1nm** snapping.
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* Basic 2D transformations (rotate, translate).
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* **Property-Based Tests:** Verify transform invariants (e.g., $90^\circ$ rotation cycles).
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3. **`geometry.components`:**
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* `Straight`, `Bend90`, `SBend`.
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* **Search Grid Snapping:** Implement 1µm snapping for expanded ports.
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* **Small S-Bends ($O < 2R$):** Logic for parametric generation.
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* **Edge Cases:** Handle $O=2R$ and $L < 1\mu m$.
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4. **Tests:**
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* Verify geometric correctness (refer to Testing Plan Section 1).
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* Unit tests for `Port` snapping and component transformations.
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## Phase 2: Collision Engine & Cost
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**Goal:** Build the R-Tree wrapper and the analytic cost function.
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1. **`geometry.collision`:** Implement `CollisionEngine`.
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* **Pre-dilation:** Obstacles/Paths dilated by $Clearance/2$.
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* **Safety Zone:** Ignore collisions within **2nm** of start/end ports.
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2. **`router.danger_map`:**
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* Implement **1µm** pre-computed proximity grid.
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* Optimize for design sizes up to **20x20mm** (< 2GB memory).
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3. **`router.cost`:** Implement `CostEvaluator`.
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* Bend cost: $10 \times (\text{Manhattan distance between ports})$.
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* Integrate R-Tree for strict checks and Danger Map for heuristic.
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4. **Tests:**
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* Verify collision detection with simple overlapping shapes (Testing Plan Section 2.1).
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* Verify Danger Map accuracy and memory footprint (Testing Plan Section 2.2).
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* **Post-Route Validator:** Implement the independent `validate_path` utility.
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## Phase 3: Single-Net A* Search
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**Goal:** Route a single net from A to B with 1nm precision.
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1. **`router.astar`:** Implement the priority queue loop.
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* State representation: `(x_µm, y_µm, theta)`.
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* Move expansion loop with 1µm grid.
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* **Natural S-Bends:** Ensure search can find $O \ge 2R$ shifts by combining moves.
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* **Look-ahead Snapping:** Actively bridge to the 1nm target when in the capture radius (10µm).
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2. **Heuristic:** Manhattan distance $h(n)$ + orientation penalty + Danger Map lookup.
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3. **Tests:**
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* Solve simple maze problems and verify path optimality (Testing Plan Section 3).
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* Verify snap-to-target precision at 1nm resolution.
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* **Determinism:** Verify same seed = same path.
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## Phase 4: Multi-Net PathFinder
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**Goal:** Implement the "Negotiated Congestion" loop for multiple nets.
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1. **`router.pathfinder`:**
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* Sequential routing -> Identify congestion -> Inflate cost -> Reroute.
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* **R-Tree Congestion:** Store dilated path geometries.
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2. **Explicit Results:** Return `RoutingResult` objects with `is_valid` and `collisions` metadata.
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3. **Tests:**
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* Full multi-net benchmarks (Testing Plan Section 4).
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* Verify rerouting behavior in crowded environments.
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## Phase 5: Visualization, Benchmarking & Fuzzing
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1. **`utils.visualization`:** Plot paths using `matplotlib`. Highlight collisions in red.
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2. **Benchmarks:** Stress test with 50+ nets. Verify performance and node limits (Testing Plan Section 5).
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3. **Fuzzing:** Run A* on randomized layouts to ensure stability.
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4. **Final Validation:** Ensure all `is_valid=True` results pass the independent `validate_path` check.
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