fieldratchet

• Turns hand-done PCB place-and-route into an executable, self-improving loop — schematic in, scored & routed KiCad board out
• The optimizer is a coupled-oscillator (Kuramoto) substrate that mutates itself and only promotes changes that lower a real geometric + field-risk score
• Ships fully offline (no kicad-cli, no network, no API keys) as an Electron app + CLI; built for the AI Engineer World's Fair Hackathon under Recursive Intelligence

Problem

Electrical design engineers place and route every board by hand — agonizing over where the buck switching node goes, how far the crystal sits from the noisy regulator, whether the USB pair stays balanced, which decoupling cap hugs which power pin — then run DRC, find the field-risk and EMI problems, rip it up, and do it again. The intuition they earn on one board never compounds back into the tool; the loop runs in the engineer's head, not in software.

Solution

fieldratchet makes that loop executable and self-improving. It parses a KiCad schematic, classifies parts and nets, places and routes a real .kicad_pcb, and scores it on a concrete objective (ratsnest length, courtyard/DRC violations, buck switch-loop area, noisy↔sensitive coupling, antenna keepout, thermal density). The twist that makes it recursive: each iteration doesn't just optimize the board — it mutates its own optimizer and keeps the mutation only if the canonical score improves and an independent field check doesn't regress. The best score is therefore monotonically non-increasing — a true ratchet — and the improved optimizer transfers to boards it has never seen.

How

• Language / runtime: TypeScript (engine + RSI loop), Electron + a CLI, Remotion for the rendered walkthrough.
• Placement substrate: a coupled-oscillator (Kuramoto) field — the netlist compiles into phase oscillators where shared nets are attractive (synchronizing → placed near) and noisy↔sensitive nets are repulsive (anti-phase → kept apart). A conditioning block (board intent + thermal hotspots + engineer feedback) biases the field; hundreds of random phase-seeds are raced and each synchronized field is decoded with coord = board_size · sigmoid(a·sin θ + b·cos θ). The mechanism is borrowed from Un-0 (coupled oscillators steered by a conditioning block) and repurposed from image synthesis into a placement optimizer.
• The promotable thing: an OscSubstrate — coupling gains, drives, damping, readout — is what the RSI loop mutates and promotes; it also synthesizes symbolic layout rules from hotspots under the same score gate.
• Independent validator: a 2.5D damped-wave EMI voxel solver that progressively refines from 4 mm full-board down to 10 µm inside hotspots of interest (added a region-of-interest window to the solver for the µm-scale zoom).
• Optional accel: a Mojo/MAX GPU custom-op kernel mirrors the Kuramoto dynamics 1:1 for large seed batches (CPU TypeScript path is the shipping default).

Tests

No unit suite — the objective is the test. Promotion is gated on the canonical score, so regressions can't be promoted by construction. A CLI bench pits the oscillator substrate against a simulated-annealing baseline across every example board, and the EMI validator runs as an independent second opinion (refinement-stability + hotspot ranking) that can veto a promotion the geometric score would have accepted.

Results

• On a 187-component autonomy mainboard (450 nets, 1,685 couplings, four 3-phase BLDC channels, dual radios, Ethernet/CAN/USB) the oscillator substrate beats simulated annealing by 78%; across the smaller boards it wins 21–70%.
• Transfer: a substrate evolved on one board makes the optimizer ~24% better on a new motor-driver board with zero new feedback.
• The EMI pass resolves field risk continuously from millimeters to 10 µm, attributing hotspots to the aggressor (switch nodes / motor phases) and victim (crystal / sensor) nets.
• The demo above (rendered with Remotion from real engine output + a real Electron capture) walks the full pipeline: layers → coupled-oscillator core → multi-scale EMI → live run → benchmark.

Lessons

The hard part wasn't the oscillator math — it was making "self-improvement" honest. A loop that grades its own homework will happily drift; gating every promotion on a fixed, externally-meaningful score plus an independent field validator is what turns "it changes" into "it gets better." If I rebuilt it, I'd compile the Mojo/MAX kernel into the live path from day one so the seed race runs on a GPU, and push the EMI solver toward a properly calibrated (not just refinement-stable) verdict.

Repo: JacobFV/fieldratchet