What is PCB Microsections

Introduction to PCB Microsections

PCB (Printed Circuit Board) microsections are thin cross-sectional slices of a PCB that are prepared and examined under a microscope to analyze the internal structure and quality of the board. This technique is widely used in the electronics industry for quality control, failure analysis, and research purposes.

PCB microsections provide valuable insights into the manufacturing process, material composition, and potential defects that may affect the performance and reliability of electronic devices. By examining the microsections, engineers and technicians can identify issues such as delamination, voids, cracks, and improper plating, which may not be visible through external inspection.

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Importance of PCB Microsections

The importance of PCB microsections cannot be overstated in the electronics industry. Here are some key reasons why PCB microsections are crucial:

Quality Control

PCB microsections are an essential tool for quality control in PCB manufacturing. By examining the internal structure of the board, manufacturers can ensure that the PCB meets the required specifications and standards. This includes verifying the thickness of copper layers, the integrity of vias and plated through-holes, and the absence of defects such as voids or delamination.

Failure Analysis

When electronic devices fail or malfunction, PCB microsections can be used to identify the root cause of the problem. By analyzing the internal structure of the board, engineers can pinpoint the specific location and nature of the defect, such as a cracked solder joint or a damaged trace. This information is crucial for developing corrective actions and improving the reliability of future products.

Research and Development

PCB microsections are also valuable for research and development purposes. By studying the internal structure of different PCB materials and designs, researchers can gain insights into the factors that affect the performance and reliability of electronic devices. This knowledge can be used to develop new materials, manufacturing processes, and design techniques that improve the quality and functionality of PCBs.

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” alt=”” class=”wp-image-136″ >

Preparing PCB Microsections

Preparing PCB microsections is a delicate process that requires specialized equipment and expertise. Here are the main steps involved in preparing a PCB microsection:

Cutting the Sample

The first step in preparing a PCB microsection is to cut a small sample from the board. This is typically done using a precision saw or a laser cutter to ensure a clean and accurate cut. The sample should be oriented perpendicular to the surface of the board to provide a cross-sectional view of the internal structure.

Mounting the Sample

Once the sample is cut, it is mounted in a plastic or epoxy resin to provide support and protection during the grinding and polishing process. The sample is placed in a mold, and the resin is poured around it and allowed to cure. The cured block is then removed from the mold and labeled for identification.

Grinding and Polishing

The mounted sample is then ground and polished to expose the internal structure of the PCB. This is done using a series of progressively finer abrasive papers and polishing cloths to remove material and create a smooth, flat surface. The grinding and polishing process must be carefully controlled to avoid damaging the sample or introducing artifacts that could interfere with the analysis.

Etching and Staining

After grinding and polishing, the sample may be etched or stained to enhance the contrast and visibility of the internal features. Etching involves exposing the sample to a chemical solution that selectively removes material from certain areas, such as the copper traces or the fiberglass substrate. Staining involves applying a dye or pigment to the sample to highlight specific features or materials.

Analyzing PCB Microsections

Once the PCB microsection is prepared, it can be analyzed using various microscopy techniques to reveal the internal structure and quality of the board. Here are some common methods used for analyzing PCB microsections:

Optical Microscopy

Optical microscopy is the most basic and widely used method for analyzing PCB microsections. It involves using a compound microscope with visible light to examine the sample at magnifications ranging from 50x to 1000x. Optical microscopy can reveal features such as the thickness and uniformity of copper layers, the presence of voids or delamination, and the quality of plated through-holes.

Scanning Electron Microscopy (SEM)

Scanning electron microscopy (SEM) is a more advanced technique that uses a focused beam of electrons to scan the surface of the sample and generate high-resolution images. SEM can reveal even finer details than optical microscopy, such as the grain structure of the copper, the presence of microcracks or inclusions, and the composition of different materials. SEM often used in conjunction with energy-dispersive X-ray spectroscopy (EDS) to analyze the elemental composition of specific areas.

Confocal Laser Scanning Microscopy (CLSM)

Confocal laser scanning microscopy (CLSM) is a specialized technique that uses a laser to scan the sample and generate three-dimensional images of the internal structure. CLSM can provide detailed information about the topography and morphology of the sample, such as the surface roughness, the depth of vias and plated through-holes, and the presence of defects or anomalies.

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Common Defects Found in PCB Microsections

PCB microsections can reveal a wide range of defects that can affect the performance and reliability of electronic devices. Here are some of the most common defects found in PCB microsections:

Voids

Voids are air pockets or gaps that can form within the PCB during the manufacturing process. They can occur in the copper layers, the dielectric material, or the solder joints. Voids can weaken the mechanical strength of the board and cause electrical failures by disrupting the flow of current.

Delamination

Delamination is the separation of the layers of the PCB, typically between the copper and the dielectric material. It can be caused by poor adhesion, thermal stress, or mechanical damage. Delamination can lead to electrical failures by creating open circuits or short circuits between the layers.

Cracks

Cracks are fractures or breaks in the PCB that can occur during manufacturing, assembly, or use. They can be caused by mechanical stress, thermal cycling, or impact damage. Cracks can disrupt the electrical continuity of the board and lead to intermittent or complete failures.

Plating Defects

Plating defects are issues with the copper plating process that can affect the quality and reliability of the PCB. Common plating defects include:

Defect Description Causes Effects
Thin plating Insufficient thickness of copper layer Improper plating parameters or contamination Increased resistance and reduced current carrying capacity
Nodules Small bumps or protrusions on the copper surface Excessive current density or contamination Reduced clearance between traces and potential for short circuits
Voids Gaps or pinholes in the copper layer Air bubbles or contamination during plating Reduced conductivity and potential for open circuits
Roughness Uneven or textured copper surface Improper agitation or contamination Increased signal loss and potential for adhesion issues

Soldermask Defects

Soldermask defects are issues with the protective coating that covers the copper traces on the PCB. Common soldermask defects include:

Defect Description Causes Effects
Pinholes Small holes or gaps in the soldermask Contamination or improper curing Reduced insulation and potential for short circuits
Delamination Separation of the soldermask from the copper Poor adhesion or mechanical stress Exposure of the copper to the environment and potential for corrosion
Discoloration Changes in the color or appearance of the soldermask Exposure to heat, light, or chemicals Cosmetic issues and potential for reduced durability

Benefits of PCB Microsections

PCB microsections offer numerous benefits for the electronics industry, including:

Improved Quality Control

By examining the internal structure of PCBs, manufacturers can identify and correct defects before they lead to failures in the field. This can help to improve the overall quality and reliability of electronic products, reducing warranty claims and customer complaints.

Faster Failure Analysis

When failures do occur, PCB microsections can help to quickly identify the root cause of the problem, reducing the time and cost of failure analysis. This can help manufacturers to develop corrective actions and prevent similar issues from occurring in the future.

Enhanced Research and Development

By studying the internal structure of different PCB materials and designs, researchers can gain valuable insights into the factors that affect performance and reliability. This can lead to the development of new materials, manufacturing processes, and design techniques that improve the functionality and durability of electronic products.

Compliance with Industry Standards

Many industry standards, such as IPC-A-600 and IPC-6012, require PCB microsections as part of the quality control and certification process. By performing microsectional analysis, manufacturers can demonstrate compliance with these standards and meet the requirements of their customers.

FAQs

What equipment is needed to prepare PCB microsections?

To prepare PCB microsections, you will need a precision saw or laser cutter to cut the sample, a mounting press and resin to encapsulate the sample, a grinder and polisher to expose the internal structure, and a microscope to analyze the sample. Specialized equipment, such as a vacuum impregnation system or an ion milling machine, may also be used for more advanced sample preparation techniques.

How long does it take to prepare a PCB microsection?

The time required to prepare a PCB microsection depends on the complexity of the sample and the desired level of detail. A basic microsection can typically be prepared in a few hours, while more advanced techniques may take several days. Factors such as the number of samples, the hardness of the materials, and the need for etching or staining can also affect the preparation time.

Can PCB microsections be used to analyze flexible circuits?

Yes, PCB microsections can be used to analyze flexible circuits, although the sample preparation process may be more challenging due to the thin and flexible nature of the material. Special techniques, such as cryogenic sectioning or laser ablation, may be used to prepare microsections of flexible circuits without damaging the sample.

How much does PCB microsectional analysis cost?

The cost of PCB microsectional analysis varies depending on the complexity of the sample, the equipment and expertise required, and the volume of samples being analyzed. Basic microsectional analysis can cost several hundred dollars per sample, while more advanced techniques can cost several thousand dollars. Many PCB Manufacturers and testing laboratories offer microsectional analysis services on a contract basis, with discounts available for high-volume orders.

Can PCB microsections detect counterfeit components?

PCB microsections can be used to detect certain types of counterfeit components, such as those with incorrect material composition or internal structure. However, microsectional analysis alone may not be sufficient to conclusively identify counterfeit components, and other techniques such as X-ray inspection, electrical testing, and chemical analysis may also be required. Proper supply chain management and traceability are also important for preventing the use of counterfeit components in PCB manufacturing.

Conclusion

PCB microsections are a powerful tool for analyzing the internal structure and quality of printed circuit boards. By providing detailed images of the board’s cross-section, microsections can reveal defects, material properties, and manufacturing processes that are critical to the performance and reliability of electronic devices.

As the electronics industry continues to evolve, with ever-increasing demands for miniaturization, high-speed performance, and reliability, PCB microsections will remain an essential technique for ensuring the quality and integrity of PCBs. By investing in the equipment, expertise, and processes required for microsectional analysis, PCB manufacturers and testing laboratories can provide valuable insights and assurances to their customers, helping to drive innovation and growth in the industry.

Ultimately, the use of PCB microsections is not just about finding defects and solving problems, but about continuously improving the design, manufacture, and performance of electronic products. By understanding the internal structure and behavior of PCBs at the microscopic level, we can unlock new possibilities for advanced electronics and create a more connected, sustainable, and prosperous future.

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