Machine Learning and Uncertainty Quantification for Semiconductor Process Equipment OEMs
SmartUQ's Machine Learning, Design of Experiments, and Uncertainty Quantification tools enable semiconductor equipment OEMs to accelerate multi-physics simulations, optimize reactor hardware designs, calibrate processes against sensor data, and characterize failure boundaries under real-world operating variability.
Key Applications Across Semiconductor Equipment Domains
Plasma Etch & PECVD Reactor Modeling
Plasma dynamics and RF power delivery involve high non-linearity, complex plasma-surface reactions, and coupled electromagnetic-fluid interactions.
- Fast Multi-Physics Emulation: SmartUQ builds high-accuracy surrogate models from computationally expensive plasma and thermal-fluid simulations, allowing engineers to quickly evaluate gas flows, temperature gradients, chamber pressures, and EM fields.
- Adaptive Failure & Pass/Fail DOE: For plasma source testing and chamber hardware (e.g., preventing RF arcing or dielectric breakdown), SmartUQ’s adaptive DOEs and Bayesian contour finding target boundaries and regions with high levels of uncertainty to map safe operating windows with fewer simulations and physical test iterations.
Wet Processing & Wafer Cleaning
Single-wafer wet etch, clean, and spin-rinse tools require tight flow uniformity and boundary-layer control to prevent defects.
- Hydrodynamic Surrogate Modeling: Accelerates multi-phase CFD runs with surrogate models to optimize nozzle placement, spray trajectories, and wafer rotation speeds.
- Global Sensitivity Analysis: Identifies which tool tolerances (e.g., chemical dispense rates, rotational velocity, rinse timing) most influence boundary layer variability and particle removal efficiency.
- Reliability-Based Design Optimization: Optimizes seal materials, valve lifecycles, and chemical chamber longevity by including manufacturing and operating uncertainties when optimizing stresses and chemical-corrosion.
Chemical Vapor Deposition (CVD & ALD)
Uniformity & Thermal Control Achieving atomic layer uniformity across the entire surface of a 300mm wafer requires precise precursor dosing and multi-zone heater tuning.
- Bayesian Statistical Calibration: Aligns complex multi-zone thermal and precursor reaction models with physical sensor arrays (e.g., multi-point thermocouples, optical emission spectroscopy) to isolate unobservable reaction kinetics and calibrate prototypes rapidly.
- Multi-Fidelity Data Fusion: Merges extensive low-fidelity 2D thermal-fluid models with sparse, expensive physical chamber measurements to build high-trust predictive models that account for tool-to-tool variations.
- Process Window Optimization: Explores high-dimensional design spaces covering gas delivery, precursor pulse timing, and vacuum pressure using advanced optimal design of experiments and Bayesian optimization tools that learn as more data is collected.
Equipment Digital Twins & Real-Time Tool Diagnostics
As processing equipment becomes more complex to meet increasingly strict tolerance requirements, predictive equipment models become a crucial part of process control.
- Digital Twin Deployment via FMUs: Export trained surrogate models as Functional Mock-up Units (FMUs) or binary objects (to use with Python or C++ APIs) capable of running in real time inside tool controller software or factory monitoring systems.
- Prognostics & Health Management: Compare and combine sensor data with physics based simulation surrogate models to monitor component degradation, evaluate thermal wear, and schedule predictive maintenance before tool drift causes defects.
Functional Emulation of Thermodynamic Simulation Case Note
SmartUQ worked with a Fortune 500 Semiconductor OEM that was using a highly detailed particle energy simulation as part of a semiconductor engineering process. The high-fidelity simulation was computationally intensive, limiting its utility, and the simulation response was a complex energy density function, making emulation difficult.
Using a combination of SmartUQ’s advanced continuous and functional emulation tools, the simulation response was emulated using a small number of simulation runs as the training sample. The emulator can accurately predict the relevant properties and shape of the particle energy density function with respect to the simulation input parameters.
The emulation process and tools demonstrated in this project allow the customer to more rapidly evaluate process changes, optimize designs, and investigate defect root causes much faster than before while using fewer computational resources.
