Using OptiSLang to Determine PCB Effective Material Properties
Eng. Iulia – Eliza Ținca
Autonomous Mobility, Continental Automotive Romania
High-performance computers for assisted and automated driving perform tasks required for perception, human vision, driving, and parking functions. Self-driving vehicle systems include some of the world's highest-performance electronic components. The insertion of new, cutting-edge technology in the automotive industry increases the demands for virtual prototyping and reliability prediction to yield high quality and high-reliability products and systems.
Zynq UltraScale+ MPSoC Powers Continental ARS540 4D Radar System
Each of these sophisticated sensors is an electronic device containing one or multiple Printed Circuit Boards (PCBs). A PCB is a multilayered assembly of patterned conductive copper traces between insulating sheets of resin-impregnated fiberglass fabric. From a mechanical point of view, the PCB is a nonhomogenous, anisotropic, viscoplastic material.
Cross-section view of the layers in a PCB and an example of copper traces detail view
In the product development stages, the simulation engineers create the virtual prototype of the product. We combine finite element analysis, FEA, with life prediction methodologies to determine the products' robustness and reliability. However, for accurate assessments, we must input accurate material parameters. Modeling the PCBs' complex structure and behavior increases unreasonably the simulation task's cost, so we must look for simplfied alternatives. Current industry practices and literature guidelines advocate the assumption of a homogenous, orthotropic, or isotropic, linear-elastic material of the PCB.
We aim to calibrate experimental frequency, acceleration, and strain measurements to an orthotropic material law. We performed 3-point bending tests for strain and stiffness measurement and a harmonic vibration test for frequency and acceleration measurement.
Experimental setup and measurements, left – static, right – dynamic
We model the tests in Ansys Workbench and parametrize the nine orthotropic material parameters and damping ratio. In OptiSLang we determine the most critical factors in PCB stiffness behavior through a sensitivity analysis. The Coefficient of Optimal Prognosis, CoP, indicates the prediction power of the approximation model. The closer to one, the better it is, meaning excellent predictability. We get a CoP greater than 99%, as well as local CoP above 95%. We consider the Metamodel of Optimal Prognosis, MoP, to accurately represent the FEA and proceed with optimization of the parameters for all experiments at once. In the optimization process, OptiSLang solves hundreds of designs through the MoP in a couple of minutes.
Simulation setup: left – static, right – dynamic
With relatively low-cost measurements and basic mechanical simulations, we are thus able to characterize the PCB material. In addition, we observed that homogenization methods such as the one within Sherlock and literature material parameters provide reasonable accuracy. All in all, we can custom define each PCB we model with minimal computational costs.
Total effects matrix - left and best design parameters – right
 D. Altavilla, "Xilinx And Continental Announce Cutting-Edge 4D Radar Platform For Autonomous Vehicles," Forbes, 23 09 2020. [Online]. Available: HERE. [Accessed 08 07 2022].
 I. E. Tinca, I. I. Ailinei and A. Davidescu, "Printed Circuit Board Orthotropic Material Calibration for Static and Dynamic Loading," in Electronics System-Integration Technology Conference, Sibiu, 2022 (in press).