Electron-beam-induced current (EBIC) analysis at device cross-sections has emerged as a powerful technique for semiconductor device characterization. Unlike traditional top-down approaches, cross-sectional EBIC directly visualizes junction profiles and depletion region behavior under various bias conditions. This paper synthesizes several years of research through five case studies demonstrating cross-sectional EBIC applications. Our methodology combines precise sample preparation techniques—including laser cutting with polishing or direct cleaving, followed by focused ion beam (FIB) fine-polishing—with variable electron beam landing energies to examine junctions at different depths. For advanced technology nodes, we emphasize the importance of EBIC resolution and noise floor optimization for reliable results. We demonstrate how the observed space charge region width correlates with doping concentration and can be modulated through reverse bias application, providing valuable insights into device characteristics.

Digital fault localization for semiconductor devices failing Automatic Test Pattern Generation (ATPG) tests can be a very challenging task, particularly when the package of the device does not lend itself towards dynamic stimulation techniques. In the case of wire-bonded Ball-Grid-Array (BGA) devices, complete electrical functionality may only be preserved when access to the die is done from the frontside of the unit. This imposes significant limitations to the applicable optical fault isolation (OFI) techniques and their resolution in highlighting an anomaly, especially in advanced technology nodes that incorporate several metal layers. This paper explores the use of digital VDDLV supply domains as a means of activating defects inside specific logic areas, as an alternative to complex electrical setups, thus overcoming the package related limitations.

In semiconductor manufacturing, the process of laser dicing can result in a loss of yield due to defects associated to the laser interaction with the sample. These defects can be difficult to identify, especially before a proper tuning of the process. Traditional investigation methods, like infrared (IR) inspection and focused-ion beam scanning electron microscopy (FIB-SEM) analysis, are labor-intensive and lack comprehensive insights. Here, we propose a robust correlative microscopy (CM) workflow integrating IR, X-ray Microscopy (XRM), and FIB-SEM tomography analyses, leveraging artificial intelligence (AI) driven algorithm for time- and quality-improved dataset reconstruction, automatic segmentation and defect site identification. Our approach streamlines defect identification, preparation, and characterization. Through AI-enhanced methodologies, as well as femtosecond (fs) laser, we optimize investigation efficiency and extract crucial information about defects properties and evolution. Our research aims to advance semiconductor failure analysis by integrating AI for enhanced defect localization and high-quality 3D dataset acquisition in the realm of laser dicing processes.

High resistance failures in P+ and N+ contact chains were traced to contacts partially filled with silicon dioxide (SiO 2 ) instead of the intended tungsten. Investigation revealed that oxygen (O 2 ) entered the deposition chamber through a faulty valve during silane gas (SiH 4 ) flow for tungsten seed deposition. This contamination triggered a gas-phase reaction producing SiO 2 particles that partially filled the contacts. Analysis of reaction kinetics explained the predominance of SiO 2 formation over tungsten deposition: the bond dissociation energy for SiO 2 formation is lower than that for tungsten, and SiO 2 -producing molecular collisions occur more frequently than tungsten-producing ones. The issue was resolved by replacing the leaking valve.