Semiconductor failure analysis employs techniques from visual inspection to advanced spectroscopy to ensure device reliability and adherence to Moore's law.
By Nidhi DhullMar 8 2024Reviewed by Megan Craig, M.Sc. Failure analysis is an essential part of a complex reliability evaluation in the semiconductor industry. In an industrial environment, failure analysis may start from a preliminary analysis through a generalized flow chart and end up with in-depth advanced techniques.1 The first step for semiconductor chips is visual inspection using an optical microscope or electrical measurements.
The detection of a failure and its rapid solution in a laboratory environment is crucial for large-scale manufacturing of miniaturized semiconductors.4 Failure analysis is important to ensure that the semiconductor industry successfully keeps up with Moore’s law, which estimates a doubling of the number of transistors in an integrated circuit about every two years.
Scanning electron microscope allows analysis of a semiconductor surface and cross-section with high resolution, revealing contamination, cracking, or thermal damage. Additionally, electrical defects in semiconductors can be identified by irradiating them with an SEM’s electron beam and detecting the current or voltage thus generated.1
Semiconductor failure analysis in terms of the thermal properties of a sample is performed using techniques like fluorescent micro-thermography, infrared thermography, and photothermal radiometry. These techniques are material-specific and give information about heat capacity, thermal conductivity, film thickness, material inhomogeneities, and delaminations.2
The atomic constitution of the impurities can be estimated using energy-dispersive X-ray spectroscopy. . It also reveals the 2D distribution of an element in comparison to the SEM image from the same area. This method is used in catastrophic failure analysis like electrode degradation or solder migration in a device.1
Case Studies and Applications Lately, AI tools are being implemented for semiconductor failure analysis. A recent study published in ACS Applied Nano Materials demonstrated the effectiveness of AI-integrated physical modeling on the electrical response of the device. It was able to reveal defects equivalent to a needle-in-a-haystack.
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