| name | circuit-optimizer |
| description | Bayesian optimization for circuit auto-tuning โ closed-loop optimizer where Claude acts as the BO engine. Sweeps gm/Id + L parameters, runs Spectre, scores against specs, and iterates. Supports progressive PVT corners. Use when optimizing circuit sizing, auto-tuning amplifier parameters, or running design-space exploration. Triggers on "optimize", "auto-tune", "bayesian", "find best sizing". |
| argument-hint | ["optimization goal","e.g. \"maximize GBW subject to PM>60deg\""] |
| allowed-tools | Bash(virtuoso *) Read Write |
Circuit Optimizer (Bayesian)
Closed-loop circuit optimization: Claude as surrogate model, virtuoso-cli as simulator.
See full design: docs/plans/2026-04-05-bayesian-optimization-design.md
When to Use
- After initial sizing (from amp-copilot or manual) needs refinement
- When multiple specs conflict and manual iteration is tedious
- When PVT robustness is needed
- "Optimize my OTA", "auto-tune this circuit", "find best sizing for these specs"
Prerequisites
- A testbench exists in Virtuoso with parameterized device sizes (desVar)
- gm/Id lookup data exists for the process (
process_data/<pdk>/)
- Simulation setup works (sim-setup skill has been run at least once)
Step-by-Step Execution
Step 1: Build Problem Definition
Gather from user or introspect from schematic:
{
"optimization": {
"testbench": {"lib": "LIB", "cell": "CELL_TB", "view": "schematic"},
"parameters": [
{"name": "gmid_M1", "type": "gmid", "device": "input_pair", "range": [8, 22], "init": 14},
{"name": "L_M1", "type": "L", "device": "input_pair", "range": [300e-9, 2e-6], "init": 500e-9},
{"name": "gmid_M3", "type": "gmid", "device": "active_load", "range": [5, 15], "init": 7},
{"name": "L_M3", "type": "L", "device": "active_load", "range": [300e-9, 2e-6], "init": 500e-9},
{"name": "Cc", "type": "comp", "range": [0.5e-12, 5e-12], "init": 2.2e-12}
],
"specs": {
"gain_db": {"min": 70, "target": 80, "weight": 1.0},
"gbw_hz": {"min": 5e6, "target": 10e6, "weight": 1.0},
"pm_deg": {"min": 55, "target": 65, "weight": 0.8},
"power_w": {"max": 200e-6, "target": 100e-6, "weight": 0.5},
"sr_Vus": {"min": 5, "weight": 0.3}
},
"measurements": {
"gain_db": {"analysis": "ac", "expr": "value(dB20(VF(\"/OUT\")) 1)"},
"gbw_hz": {"analysis": "ac", "expr": "cross(dB20(VF(\"/OUT\")) 0 1 \"falling\")"},
"pm_deg": {"analysis": "ac", "expr": "value(phase(VF(\"/OUT\")) cross(dB20(VF(\"/OUT\")) 0 1 \"falling\")) + 180"},
"power_w": {"analysis": "dcOp", "expr": "value(IDC(\"/V0/PLUS\")) * 3.3"},
"sr_Vus": {"analysis": "tran", "expr": "slewRate(VT(\"/OUT\")) / 1e6"}
},
"corners": {
"phase1": [{"model": "tt", "temp": 27}],
"phase2": [{"model": "tt", "temp": 27}, {"model": "ff", "temp": 27}, {"model": "ss", "temp": 27}],
"phase3": [{"model": "tt", "temp": 27}, {"model": "ff", "temp": -40}, {"model": "ss", "temp": 125}]
},
"budget": {"max_iterations": 50, "max_sim_time_min": 120}
}
}
Parameter types:
gmid: gm/Id ratio. Range typically [5, 25]. Converted to W via lookup.L: channel length. Range [Lmin, 2u].comp: compensation element (Cc, Rz). Direct desVar.
Spec fields:
min: hard lower bound (constraint)max: hard upper bound (constraint)target: optimization goal (only matters after feasibility)weight: relative importance among targets
Step 2: Convert gm/Id to W
For each gmid parameter, compute W using the gm/Id lookup table:
virtuoso skill exec 'RB__gmid_target = 14.0' --format json
virtuoso skill exec 'RB__L = 500e-9' --format json
For the optimizer, the flow is:
- User specifies required
gm for each device (from spec decomposition)
- Optimizer tunes
gmid and L
- W = gm / (gmid * Id_norm(gmid, L))
If gm is not fixed, use current budget: Id = I_budget / num_branches, then W = Id / Id_norm.
Step 3: Run One Iteration
virtuoso skill exec 'desVar("W_M1" 3.1e-6)' --format json
virtuoso skill exec 'desVar("L_M1" 500e-9)' --format json
virtuoso skill exec 'desVar("W_M3" 1.4e-6)' --format json
virtuoso sim run --analysis dc --param saveOppoint=t --timeout 120 --format json
virtuoso sim run --analysis ac --start 1 --stop 1e10 --dec 20 --timeout 120 --format json
virtuoso sim run --analysis tran --stop 20u --timeout 120 --format json
virtuoso sim measure --analysis ac \
--expr 'value(dB20(VF("/OUT")) 1)' \
--expr 'cross(dB20(VF("/OUT")) 0 1 "falling")' \
--format json
virtuoso sim measure --analysis dcOp \
--expr 'value(IDC("/V0/PLUS")) * 3.3' \
--format json
virtuoso skill exec 'modelFile(list("/path/models.lib" "ff"))' --format json
Step 4: Score the Result
SCORING FUNCTION (compute in Claude, not SKILL):
1. Feasibility check:
For each spec with min/max:
violation = max(0, spec_min - measured) / spec_min # undershoot
+ max(0, measured - spec_max) / spec_max # overshoot
feasibility_cost = sum of all violations
2. If infeasible (feasibility_cost > 0):
cost = 1000 + feasibility_cost
3. If feasible:
target_cost = sum(weight_i * |1 - measured_i / target_i|) for specs with targets
cost = target_cost
4. For Phase 2/3 (multi-corner):
cost = max(cost across all corners)
Step 5: Update History
Write/update the history JSON file:
History JSON structure:
{
"meta": {"cell": "...", "pdk": "...", "phase": 1, "iteration": 12, "status": "running"},
"problem": {"...problem definition..."},
"best": {
"iteration": 9,
"params": {"gmid_M1": 13.2, "L_M1": 6.5e-7},
"derived": {"W_M1": 3.1e-6, "Id_M1": 14.2e-6},
"results": {"tt_27": {"gain_db": 74.2, "gbw_hz": 8.1e6}},
"cost": 0.12,
"feasible": true
},
"history": [
{"iter": 0, "phase": 1, "params": {}, "results": {}, "cost": 1000.35, "feasible": false, "note": "gain below min"}
]
}
Step 6: Suggest Next Point (Surrogate Reasoning)
This is where Claude acts as the Bayesian optimizer. Follow this protocol:
Every iteration, reason through:
-
Review history โ Sort by cost. Identify top 3-5 points.
-
Identify trends โ Which parameters improved results when changed?
- "Increasing L_M1 from 300n to 500n improved gain by 8dB"
- "gmid_M3 below 6 always causes PM violation"
-
Choose strategy (3:1 exploit:explore ratio):
- Exploit (iterations 0,1,2, 4,5,6, 8,9,10, ...): Perturb best point.
Pick 1-3 parameters that most correlate with improvement.
Adjust by 10-20% in the promising direction.
- Explore (iterations 3, 7, 11, ...): Sample a point far from all visited.
Use midpoints of unvisited parameter subregions.
-
Bound check โ Ensure all parameters within range.
-
Physical check โ gm/Id in [5, 25], L >= Lmin, W > 0.
Step 7: Decide Continue/Phase-Up/Stop
IF all specs met AND cost < 0.05:
โ CONVERGE. Report final sizing.
IF no improvement for 5 consecutive iterations:
IF current phase < 3:
โ PHASE UP. Move to next corner set. Reset stall counter.
ELSE:
โ STOP. Report best achievable.
IF iteration >= budget.max_iterations:
โ STOP. Report best.
OTHERWISE:
โ CONTINUE to next iteration.
Progress Report Format
Print after every iteration:
โโ Iteration 12/50 (Phase 1: TT) โโโโโโโโโโโโโโโโโโโโโโ
Parameters: gmid_M1=13.2 L_M1=650n gmid_M3=8.1 L_M3=500n Cc=2.8p
Derived: W_M1=3.1um W_M3=1.4um Id_M1=14.2uA
Results vs Spec:
gain_db: 74.2 (min:70 โ target:80 โณ)
gbw_hz: 8.1M (min:5M โ target:10M โณ)
pm_deg: 62 (min:55 โ target:65 โณ)
power_w: 178u (max:200u โ target:100u โ)
sr_Vus: 6.3 (min:5 โ)
Cost: 0.31 (feasible โ) Best: 0.12 @ iter 9
Strategy: Exploit โ reducing gmid_M6 to lower power
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Final Report
When optimization completes, report:
โโ OPTIMIZATION COMPLETE โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Status: CONVERGED after 31 iterations (Phase 2)
Total simulation time: 47 min
Best Design (iteration 28):
gmid_M1=12.8 L_M1=700n โ W_M1=3.5um Id_M1=15.1uA
gmid_M3=7.5 L_M3=500n โ W_M3=1.6um Id_M3=15.1uA
gmid_M6=9.2 L_M6=350n โ W_M6=5.8um Id_M6=48uA
Cc=2.5pF
Corner Results:
gain_db gbw_hz pm_deg power_w sr_Vus
tt_27: 76.3 9.2M 63 185u 7.1 โ
ff_27: 70.1 12.8M 56 220u 9.2 โ (power marginal)
ss_27: 81.2 6.1M 68 158u 5.2 โ
All specs met across Phase 2 corners.
History: process_data/smic13mmrf/opt_history/miller_ota_tb_20260405.json
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Resume Support
To resume a previous optimization:
ls process_data/*/opt_history/*.json
Bandgap IP Support
For bandgap circuits, use the bundled script scripts/run_bandgap_sweep.py.
Bandgap FOM (Figure of Merit)
feasibility_cost = max(0, (Vbg_target - Vbg_measured) / Vbg_target) # Vbg too low
+ max(0, (Vbg_measured - Vbg_target) / Vbg_target) # Vbg too high
+ max(0, (PSRR_min - PSRR_measured) / PSRR_min) # PSRR insufficient
+ max(0, (TC_measured - TC_max) / TC_max) # TC too high
If infeasible: cost = 1000 + feasibility_cost
If feasible: cost = w_vbg * |1 - Vbg/Vbg_target|
+ w_psrr * |1 - PSRR/PSRR_target| * 0.5
+ w_tc * |1 - TC_target/TC_measured| * 0.3
Default weights: w_vbg=1.0, w_psrr=0.5, w_tc=0.3
Bandgap Workflow
cat > bandgap.yaml << 'EOF'
ip_type: bandgap
target:
Vbg: 1.20
PSRR: 80
TC: 20
params:
W:
min: 1e-6
max: 10e-6
step: 1e-6
L:
min: 0.18e-6
max: 1e-6
step: 0.18e-6
corner: tt
EOF
python ${CLAUDE_SKILL_DIR}/scripts/run_bandgap_sweep.py run \
--spec bandgap.yaml --netlist template.scs --timeout 600
python ${CLAUDE_SKILL_DIR}/scripts/run_bandgap_sweep.py status bg-a3c4f9
python ${CLAUDE_SKILL_DIR}/scripts/run_bandgap_sweep.py report bg-a3c4f9 --output bg_report.md
Spec YAML Reference
ip_type: bandgap
target:
Vbg: 1.20
PSRR: 80
TC: 20
params:
W:
min: 1e-6
max: 10e-6
step: 1e-6
L:
min: 0.18e-6
max: 1e-6
step: 0.18e-6
corner: tt
evolution_strategy: balanced
Strategy Presets
evolution_strategy controls the exploration/exploitation balance and convergence tolerance.
| Strategy | When to use | Surrogate behavior | Convergence gate |
|---|
balanced (default) | Normal sizing run | Exploit best ร2, explore new ร1 in each batch | ฮ < 1% for 2 consecutive iterations |
innovate | First sizing of new topology; need broad landscape view | Explore ร3, exploit ร1; accept 10% FOM degradation if coverage improves | ฮ < 5%; max 30 iterations |
harden | Known-good sizing; tighten to meet worst corner | Exploit only (ยฑ10% around best point); reject any candidate outside current feasible region | ฮ < 0.2% for 3 consecutive iterations |
repair-only | One spec failing; all others nominal | Freeze all params except the one linked to failing spec; minimize that spec's violation only | Failing spec enters feasible region |
Surrogate reasoning adjustments per strategy:
balanced:
- Evaluate top-3 candidates from surrogate model
- Pick 2 that exploit (lowest predicted cost near current best)
- Pick 1 that explores (highest uncertainty region)
innovate:
- Latin hypercube sample 5 new points across full parameter range
- Keep 1 best from previous iteration (anchor)
- Looser feasibility: skip corners beyond "tt" for first 3 iterations
harden:
- Grid search ยฑ10% around best known point (step = original_step / 5)
- All corners mandatory from iteration 1
- Reject candidates where any spec degrades vs. current best
repair-only:
- Identify failing spec โ find its dominant parameter (sensitivity analysis)
- Fix all other params at their best-known values
- Sweep only the dominant parameter in [current - 3ฯ, current + 3ฯ]
- Stop when failing spec first enters [min, max] feasible window
In the history JSON, record the strategy:
{
"evolution_strategy": "harden",
"iteration": 5,
"best_cost": 0.032,
...
}
Artifact Capture Whitelist
ๆน้ sweep ๆฏไธช็น้ฝไผไบง็ๅคง้ PSF ๆไปถ๏ผdc/ใac/ใtran/ใoppoint/โฆ๏ผ๏ผ
้ป่ฎคๅ
จ้จไฟ็ไผๅฏผ่ด็ฃ็็็ธ๏ผdebug ไน้พๆพใ็จ YAML artifacts ๅญๆฎตๅฃฐๆๅชไฟ็ๅชไบๅๆ็ฎๅฝ๏ผ
artifacts:
keep:
- "ac"
- "dcOpInfo"
run_bandgap_sweep.py ๅจๆฏไธช็นไปฟๅฎๅๆ นๆฎ artifacts.keep ๆธ
็๏ผ
for dir in psf_root.iterdir():
if dir.name not in spec.get("artifacts", {}).get("keep", [...all...]):
shutil.rmtree(dir)
ไฝฟ็จๅๅ๏ผ
- ๅชๆต DC ๅทฅไฝ็น โ
keep: ["dcOpInfo"]
- ๅชๆต AC ๆง่ฝ โ
keep: ["ac", "dcOpInfo"]๏ผdcOpInfo ้ๅธธๅพๅฐ๏ผๅปบ่ฎฎไธ่ตท็๏ผ
- ๅฎๆด PVT โ
keep: ["ac", "dcOpInfo", "tran"]๏ผnoise ๅพๅคงๅฏไปฅไธ็
- ่ฐ่ฏๅไธชๅคฑ่ดฅ็นๆถ๏ผไธดๆถๅปๆ
artifacts ๅญๆฎต๏ผไฟ็ๅ
จ้จ
็ฝๅๅ ๅชๅฝฑๅ็ฃ็๏ผไธๅฝฑๅๆต้โโvcli sim measure ไปๅ
ๅญ/ๆดป่ท PSF ่ฏปๅ๏ผไปฟ็ๆ้ด PSF ไปๅฎๆด๏ผๆธ
็ๅจ่ฏปๅฎ็ปๆไนๅ่ฟ่กใ
When to Use Script vs Manual Loop
| Scenario | Use |
|---|
| Bandgap W/L sweep | run_bandgap_sweep.py run |
| OTA multi-param (gmid, L, Cc) | Manual iteration (this skill) |
| PVT corners on known sizing | vcli sim corner |
| Arbitrary IP first sizing | /spec-driven-circuit-design then this skill |
Integration with Other Skills
| Skill | Integration Point |
|---|
| amp-copilot | Provides initial sizing (iteration 0) |
| gm-over-id | Lookup W from gm/Id + L |
| sim-setup | Configure testbench before first iteration |
| sim-run | Execute Spectre each iteration |
| sim-measure | Extract spec values |
| spec-driven-circuit-design | Provides spec template and decomposition |
