This chapter assesses the potential impact of neural networks on package-level failure analysis, the challenges presented by next-generation semiconductor packages, and the measures that can be taken to maximize FA equipment uptime and throughput. It presents examples showing how neural networks have been trained to detect and classify PCB defects, improve signal-to-noise ratios in SEM images, recognize wafer failure patterns, and predict failure modes. It explains how new packaging strategies, particularly stacking and disintegration, complicate fault isolation and evaluates the ability of various imaging methods to locate defects in die stacks. It also presents best practices for sample preparation, inspection, and navigation and offers suggestions for improving the reliability and service life of tools.

This article introduces the wafer-level fault localization failure analysis (FA) process flow for an accelerated yield ramp-up of integrated circuits. It discusses the primary design considerations of a fault localization system with an emphasis on complex tester-based applications. The article presents examples that demonstrate the benefits of the enhanced wafer-level FA process. It also introduces the setup of the wafer-level fault localization system. The application of the wafer-level FA process on a 22 nm technology device failing memory test is studied and some common design limitations and their implications are discussed. The article presents a case study and finally introduces a different value-add application flow capitalizing on the wafer-level fault localization system.

As semiconductor feature sizes have shrunk, the technology needed to encapsulate modern integrated circuits has expanded. Due to the various industry changes, package failure analyses are becoming much more challenging; a systematic approach is therefore critical. This article proposes a package failure analysis flow for analyzing open and short failures. The flow begins with a review of data on how the device failed and how it was processed. Next, non-destructive techniques are performed to document the condition of the as-received units. The techniques discussed are external optical inspection, X-ray inspection, scanning acoustic microscopy, infrared (IR) microscopy, and electrical verification. The article discusses various fault isolation techniques to tackle the wide array of failure signatures, namely IR lock-in thermography, magnetic current imaging, time domain reflectometry, and electro-optical terahertz pulse reflectometry. The final step is the step-by-step inspection and deprocessing stage that begins once the defect has been imaged.

In embedded systems, the separation between system level, board level, and individual component level failure analysis is slowly disappearing. In order to localize the initial defect area, prepare the sample for root cause analysis, and image the exact root cause, the overall functionality has to be maintained during the process. This leads to the requirement of adding additional techniques that help isolate and image defects that are buried deeply within the board structure. This article demonstrates an approach of advanced board level failure analysis by using several non-destructive localization techniques. The techniques considered for advanced fault isolation are magnetic current imaging for shorts and opens; infrared thermography for electrical shorts; time-domain-reflectometry for shorts and opens; scanning acoustic microscopy; and 2D/3D X-Ray microscopy. The individual methods and their operational principles are introduced along with case studies that will show the value of using them on board level defect analysis.

The scanning acoustic microscope (SAM) is an important tool for development of improved molded and flip chip packages. The SAM used for integrated circuit inspection is a hybrid instrument with characteristics of both the Stanford SAM and the C-scan recorder. This chapter presents the historical development of SAM for integrated circuit package inspection, SAM theory, and analysis considerations. Case studies are presented to illustrate the practical applications of SAM. Other non-destructive imaging tools are briefly discussed, as well as SAM challenges and methods including spectral signature analysis and GHz-SAM.

This chapter presents several materials and processes related to soldering technology. It first provides information on lead-free solders, followed by sections devoted to flip-chip processes, diffusion soldering, and modeling. Scanning acoustic microscopy and fine-focus x-ray techniques are also discussed. The chapter describes several evaluation procedures and tests developed to measure solderability and standards for process calibration. The chapter also describes the characteristics of reinforced solders, amalgams used as solders, and other strategies to boost the strength of solders. Further, the chapter considers methods for quantifying the mechanical integrity of joints and predicting their dimensional stability under specified environmental conditions. It discusses the effects of rare earth elements on the properties of solders. The chapter concludes with information on advanced joint characterization techniques.