Multi-Objective Recipe Optimizer

Particle Swarm Optimization (PSO) algorithm for simultaneous optimization of thickness, uniformity, stress, and composition by tuning temperature, pressure, gas flows, RF power, and deposition time

Overview

What This Tool Does

This advanced optimizer uses Particle Swarm Optimization (PSO) to find optimal CVD/PVD process recipes that simultaneously satisfy multiple objectives. The algorithm simulates a swarm of particles exploring the parameter space, each representing a potential recipe. Particles communicate and converge toward optimal solutions using cognitive and social learning.

Key Features

5 Process Variables

Temperature (300-1200°C), Pressure (0.1-760 Torr), Gas flows (0-5000 sccm), RF Power (0-2000W), Deposition time (1-300 min)

4 Objectives

Target thickness, Minimize non-uniformity, Control film stress, Achieve composition targets

Pareto Optimization

Multi-objective trade-off analysis, 3D Pareto front visualization, Non-dominated solution ranking

PSO Algorithm

Swarm size: 30-100 particles, Iterations: 50-500, Adaptive inertia weight, Convergence tracking

Important Notes

  • Optimization typically requires 100-300 iterations for convergence
  • Physical models are semi-empirical approximations - validate experimentally
  • Pareto front shows trade-offs between conflicting objectives
  • Multiple optimal solutions exist - select based on process constraints

Target Specifications

Negative = compressive, Positive = tensile
For binary films (e.g., SiN₄: N content)

Objective Weights

Adjust importance of each objective (higher weight = higher priority)

5.0
8.0
6.0
4.0

Process Parameter Bounds

PSO Algorithm Settings

Algorithm Tuning Tips

  • Swarm Size: Larger swarms explore more thoroughly but run slower (50-100 typical)
  • Inertia Weight: Higher values favor exploration, lower values favor exploitation (0.4-0.9 typical)
  • Cognitive Coeff: Particle's tendency to return to its personal best (1.5-2.0 typical)
  • Social Coeff: Particle's tendency to move toward swarm's global best (1.5-2.0 typical)

Run Optimization

Optimization Visualizations

Convergence History

3D Pareto Front

Trade-off surface between thickness error, non-uniformity, and stress deviation

Swarm Evolution

Particle positions in parameter space over iterations

Multi-Objective Fitness

Parameter Sensitivity Analysis

Impact of each parameter on overall fitness

Fitness Distribution

Parameter Correlation Heatmap

Objective Trade-offs (2D Projections)