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TM

Ensuring Optimal PCB Design:

The Importance of Early Thermal Analysis

Abstract:

Thermal analysis plays a critical role in achieving a reliable and efficient Printed Circuit Board (PCB) design. This paper explores the significance of performing thermal analysis early in the design process, highlighting the benefits of proactive thermal management. It introduces the fiXtress® Mini Thermal Analysis module, a powerful tool for estimating temperature rise and identifying potential thermal issues before PCB layout and component placement are finalized.

Keywords: Thermal Analysis, PCB Design, Component De-rating, MTBF, fiXtress® Mini Thermal Analysis

1. Introduction

Reliable thermal management is essential for ensuring the functionality and longevity of electronic devices. In PCB design, thermal analysis plays a crucial role in achieving optimal performance and preventing component failures. This analysis lays the groundwork for other critical processes like component de-rating and Mean Time Between Failures (MTBF) calculations.

2. Benefits of Early Thermal Analysis

Performing thermal analysis early in the design process, alongside component de-rating analysis, offers several advantages:

  • Early Identification of Stressed Components: By proactively analyzing thermal behavior, engineers can identify components that may be under potential thermal stress due to self-heating. This allows for adjustments to be made before finalizing the PCB layout and component placement.

  • Optimal Component Selection: Early thermal analysis enables the selection of appropriate component sizes based on their power dissipation and thermal characteristics. This prevents over-engineering with unnecessarily large components or using smaller components that could experience thermal stress.

  • Cost-Effective Design Changes: Early detection of thermal issues allows for adjustments to be made to the layout before the PCB is manufactured. This avoids costly rework and delays associated with addressing thermal problems after PCB fabrication.

3. fiXtress® Mini Thermal Analysis

The fiXtress® Mini Thermal Analysis module is a valuable tool for early thermal assessment in PCB design. Since the PCB layout is not yet finalized, this module estimates the board's temperature rise based on the self-heating characteristics of individual components.

3.1 Functionality of fiXtress® Mini Thermal Analysis

The fiXtress® Mini Thermal Analysis module operates through the following steps:

  • Precise Power Dissipation: The module takes into account the exact power dissipation of each component on the PCB.

  • Temperature Rise Estimation: Based on the power dissipation data, the module estimates the "average temperature rise above the cold-plate temperature" (dT°C).

  • Thermal Model Creation: Utilizing the thermal data and package types of relevant components, fiXtress creates a thermal model and calculates the dT°C.

3.2 Example: Applying fiXtress® Mini Thermal Analysis

Here's a practical example demonstrating the application of fiXtress® Mini Thermal Analysis:

  • Scenario: PCB cold-plate temperature (T°C) is 71°C.

  • fiXtress® Analysis: The module calculates the dT°C to be 6.4°C.

  • Estimated Average Component Temperature: This translates to an average component temperature of (71°C + 6.4°C) = 77.4°C.

  • Internal Component Heat Dissipation: The tool additionally considers the internal heat dissipation of each component (e.g., Tj = 83.4°C for component U3).

 

4. Validation and Conclusion

The accuracy of the fiXtress® Mini Thermal Analysis results is validated by comparing them with the temperature map generated by a separate 3D thermal simulation tool. The close match between the results demonstrates the effectiveness of the fiXtress® Mini Thermal Analysis module in providing a reliable early assessment of PCB thermal behavior.

​By incorporating the fiXtress® Mini Thermal Analysis feature into the design process, engineers can proactively identify potential thermal issues and make informed decisions regarding component selection and layout optimization. This proactive approach leads to a more efficient and reliable PCB design, ultimately enhancing the performance and longevity of the final product.

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Automated MTBF Prediction & Thermal Analysis for Single and Multi-PCB Designs using Industry-Leading Patented AI Technology

Key Features:

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  • Conducts in-depth derating analysis by leveraging machine learning algorithms. 

  • Predicts MTBF (Mean Time Between Failures) for single boards or entire systems. This ensures that your electronics last longer and perform flawlessly.

  • Predicts Board Temperature: fiXtress® estimates how much your PCB's temperature will rise due to heat generation, enabling optimal thermal performance in your design.

How It Works:

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  • fiXtress® employs electrical stress analysis on components within your circuit design to perform Component De-rating.

  • The stress input is utilized for predicting MTBF, ensuring robust reliability in your system.

  • Calculation of board self-heating is conducted by considering component power dissipation and thermal resistance, optimizing thermal performance.

  • Component-rated values and thermal resistance data are stored in a comprehensive library for convenient and efficient future utilization.

Benefits:

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  • Exceeding Industry Standards: Achieve industry-leading MTBF targets, cultivate client trust, and design with confidence, surpassing conventional benchmarks.

  • Seamless Integration: Effortlessly connect with popular design tools through the ECAD Plug-In for a streamlined workflow, enhancing collaboration and productivity.

  • Ready-to-Use Standards: Utilize readily available and customizable de-rating standards for efficient reliability analyses, facilitating swift decision-making and optimization.

  • Industry Compliance: Ensure your design adheres to industry standards, safeguarding product integrity and compliance with regulatory requirements.

  • Time Efficiency: Expedite design processes with reduced need for manual data entry, courtesy of the built-in components library within the system, coupled with quick and accurate results powered by AI, enhancing overall efficiency and productivity.

  • Precision in Analysis: Accurately identify stressed components, proactively preventing unexpected breakdowns and ensuring robust system performance through precise analysis techniques.

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