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Jan Petykiewicz 2026-03-30 15:32:29 -07:00
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README.md
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# inire: Auto-Routing for Photonic and RF Integrated Circuits
`inire` is a high-performance auto-router designed specifically for the physical constraints of photonic and RF integrated circuits. It utilizes a Hybrid State-Lattice A* search combined with "Negotiated Congestion" (PathFinder) to route multiple nets while maintaining strict geometric fidelity and clearance.
`inire` is a high-performance auto-router designed specifically for the physical constraints of photonic and RF integrated circuits. It uses a Hybrid State-Lattice A* search combined with negotiated congestion to route multiple nets while maintaining strict geometric fidelity and clearance.
## Key Features
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* **Analytic Correctness**: Every move is verified against an R-Tree spatial index of obstacles and other paths.
* **1nm Precision**: All coordinates and ports are snapped to a 1nm manufacturing grid.
* **Safety & Proximity**: Incorporates a "Danger Map" (pre-computed distance transform) to maintain optimal spacing and reduce crosstalk.
* **Locked Paths**: Supports treating existing geometries as fixed obstacles for incremental routing sessions.
* **Locked Routes**: Supports treating prior routed nets as fixed obstacles in later runs.
## Installation
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## Quick Start
```python
from inire.geometry.primitives import Port
from inire.geometry.collision import CollisionEngine
from inire.router.danger_map import DangerMap
from inire.router.cost import CostEvaluator
from inire.router.astar import AStarContext
from inire.router.pathfinder import PathFinder
from inire import NetSpec, ObjectiveWeights, Port, RoutingOptions, RoutingProblem, SearchOptions, route
# 1. Setup Environment
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=(0, 0, 1000, 1000))
danger_map.precompute([]) # Add polygons here for obstacles
# 2. Configure Router
evaluator = CostEvaluator(
collision_engine=engine,
danger_map=danger_map,
greedy_h_weight=1.2
problem = RoutingProblem(
bounds=(0, 0, 1000, 1000),
nets=(
NetSpec("net1", Port(0, 0, 0), Port(100, 50, 0), width=2.0),
),
)
context = AStarContext(
cost_evaluator=evaluator,
bend_penalty=10.0
options = RoutingOptions(
search=SearchOptions(
bend_radii=(50.0, 100.0),
greedy_h_weight=1.2,
),
objective=ObjectiveWeights(
bend_penalty=10.0,
),
)
pf = PathFinder(context)
# 3. Define Netlist
netlist = {
"net1": (Port(0, 0, 0), Port(100, 50, 0)),
}
run = route(problem, options=options)
# 4. Route
results = pf.route_all(netlist, {"net1": 2.0})
if results["net1"].is_valid:
if run.results_by_net["net1"].is_valid:
print("Successfully routed net1!")
```
For incremental workflows, feed prior routed results back into a new `RoutingProblem` via `locked_routes` using `RoutingResult.as_locked_route()`.
## Usage Examples
For detailed visual demonstrations and architectural deep-dives, see the **[Examples README](examples/README.md)**.
@ -82,11 +72,11 @@ Full documentation for all user-tunable parameters, cost functions, and collisio
2. **90° Bends**: Fixed-radius PDK cells.
3. **Parametric S-Bends**: Procedural arcs for bridging small lateral offsets ($O < 2R$).
For multi-net problems, the **PathFinder** loop handles rip-up and reroute logic, ensuring that paths find the globally optimal configuration without crossings.
For multi-net problems, the negotiated-congestion loop handles rip-up and reroute logic, ensuring that paths find the globally optimal configuration without crossings.
## Configuration
`inire` is highly tunable. Every major component (Router, CostEvaluator, PathFinder) accepts explicit named arguments in its constructor to control expansion rules, cost weights, and convergence limits. See `DOCS.md` for a full parameter reference.
`inire` is highly tunable. The public API is `RoutingProblem` plus `RoutingOptions`, routed via `route(problem, options=...)`. Search internals remain available only for internal tests and development work; they are not a supported integration surface. See `DOCS.md` for a full parameter reference.
## License