Matlab Optimize Pcb DesignSAFE

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---
name: matlab-optimize-pcb-design
description: "Optimize RF PCB dimensions for bandwidth, return loss, or area via patternsearch and surrogateopt with constraints. TRIGGER: user asks to optimize an RF PCB component for performance (bandwidth, return loss, insertion loss, area) or apply constraints to a design. Invoke BEFORE writing optimization code — RF PCB Toolbox has a built-in optimize() function that differs from generic fmincon/ga approaches. SKIP: designing a component from scratch without an optimization objective (use the specific matlab-design-pcb-* skill), EM analysis without optimization (use matlab-analyze-em), material/stackup setup only (use matlab-manage-pcb-material)."
license: https://www.mathworks.com/content/dam/mathworks/license/pmrl/license.md
metadata:
  author: MathWorks
  version: "1.0"
---

# Optimizing RF PCB Designs

## Critical: Always Use optimize() First

**The RF PCB Toolbox provides a built-in `optimize()` function that handles all standard optimization scenarios.** Always use it as the first approach — do NOT write manual optimization loops with `patternsearch`, `surrogateopt`, `fmincon`, or `ga` called directly unless `optimize()` is demonstrably insufficient for the specific problem.

## When to Use

- Tuning filter, coupler, or splitter dimensions for return loss, bandwidth, or area
- Running `optimize()` on catalog objects with bounds and constraints
- Using SADEA/TR-SADEA, patternsearch, or surrogateopt via the `optimize()` interface
- Defining custom objective functions over S-parameter responses

## When NOT to Use

- Designing components from scratch — use `matlab-design-pcb-filter`, `matlab-design-pcb-coupler`, etc. first, then optimize
- Running EM analysis without optimization — use `matlab-analyze-em`
- Parameter sweeps without an objective — just loop over `sparameters` calls directly
- Antenna-only optimization without PCB context — use Antenna Toolbox docs

## Typical Workflow

1. **Before:** A design skill (`matlab-design-pcb-filter`, `matlab-design-pcb-transmission-line`, etc.) — create the initial component; `matlab-analyze-em` — baseline S-parameters
2. **This skill:** Define objective, bounds, constraints; run `optimize()`
3. **After:** `matlab-analyze-em` — validate optimized design → `matlab-integrate-pcb-circuit` — cascade into system → `matlab-write-pcb-layout` — export Gerber

## Quick Reference

| Task | Code |
|------|------|
| Design at frequency | `obj = design(ObjectType, fc)` |
| Optimize | `optObj = optimize(obj, freq, objective, props, bounds, solver)` |
| With constraints | `optimize(..., 'Constraints', constraints)` |
| With options | `optimize(..., 'OptimizerOptions', opts)` |

## Algorithm Overview

| Algorithm | Source | Best For | Typical Time |
|---|---|---|---|
| `sadea` | Antenna Toolbox (SADEA) | Default global search, few EM evaluations | 5--30 min |
| `trsadea` | Antenna Toolbox (TR-SADEA) | Trust-region variant, better local refinement | 5--30 min |
| `patternsearch` | Global Optimization Toolbox | Derivative-free, smooth/noisy EM objectives, fine-tuning | 2--15 min |
| `surrogateopt` | Global Optimization Toolbox | Expensive black-box functions, many variables, global optimum | 10--60 min |

- **Start with SADEA** -- it requires no additional toolbox beyond Antenna Toolbox and handles most RF/PCB optimization problems well.
- **Use `patternsearch` for fine-tuning** -- after SADEA finds a good region, refine with patternsearch starting from the SADEA result.
- **`surrogateopt` for expensive models** -- builds a surrogate model and is more sample-efficient when each EM solve is costly.

## Timing and Practical Considerations

- **MoM simulation per evaluation**: 2--30 seconds for simple components, minutes for high-order filters
- **Behavioral mode**: Use `pcbElement(comp, Behavioral=true)` for fast analytical evaluations during optimization sweeps
- **Iteration budget**: Set `MaxIterations` to 20--50 when running interactively to stay responsive; use 100--200 for batch runs
- **Parallel computing**: Set `UseParallel=true` if Parallel Computing Toolbox is available for significant speedup with patternsearch and surrogateopt

## design() — Initial Sizing

The `design` function sizes any catalog component for a target frequency:

```matlab
ws = design(wilkinsonSplitter, 3e9);
bl = design(couplerBranchline, 5e9);
fh = design(filterHairpin, 2.4e9);
ms = design(microstripLine, 3e9);
```

This produces a starting point for subsequent optimization. All geometric parameters are auto-computed for the target frequency.

## optimize() Syntax

```matlab
optObj = optimize(obj, freq, objective, properties, bounds, solver, ...
    'Constraints', constraints, 'OptimizerOptions', opts)
```

| Argument | Type | Description |
|----------|------|-------------|
| `obj` | Object | Catalog object (initial design) |
| `freq` | Scalar or vector | Single frequency or frequency vector for optimization |
| `objective` | String | Built-in or custom objective function name |
| `properties` | Cell array | Property names to vary |
| `bounds` | 2-element cell | `{[lower1 lower2 ...]; [upper1 upper2 ...]}` |
| `solver` | String | `'patternsearch'`, `'surrogateopt'`, `'sadea'`, or `'trsadea'` |
| `constraints` | Struct | S-parameter constraints (optional NV pair) |
| `opts` | optimoptions | Optimizer options object created with `optimoptions()` |

## Built-in Objectives

| Objective String | Minimizes/Maximizes |
|-----------------|---------------------|
| `"minimizeArea"` | Minimizes board area |
| `"maximizeBandwidth"` | Maximizes -10 dB return loss bandwidth |
| `"maximizeReturnLoss"` | Maximizes worst-case return loss |
| `"minimizeBandwidth"` | Narrows the passband |

## Example: Minimize Area with Constraints

```matlab
obj = design(wilkinsonSplitter, 3e9);

props = {'SplitLineLength', 'SplitLineWidth', 'Resistance', ...
         'PortLineLength', 'GroundPlaneWidth'};
bounds = {[10e-3 1e-3  50 2e-3  10e-3];    % Lower bounds
          

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

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