Research Topics

SIRI Lab

System Integration

Packaging essentially requires the combination of electronic parts (transistors, connections, insulation) smoothly working in tandem and, if possible, enhancing the device function. This is why the buzzword heterogenous integration is at the core of advanced packaging. By finding the optimum method of integrating the parts, an improved modern device is made. In SIRI lab we use certain methods like active power cycling, heat management, and AI image verification to make the best semiconductor devices.

Active Power Cycling

Purpose: Reliability tests on power electronic packages are crucial for ensuring the durability and safety of electronic devices. These tests assess the package’s ability to withstand various stressors such as temperature fluctuations, mechanical shocks, and electrical overloads. By subjecting packages to rigorous conditions, we can identify potential weaknesses and failure before they impact the end product. This enhances the reliability of electronic devices by delivering products that perform consistently under diverse and harsh operating conditions.

Heat Management

Purpose: Research on advanced packaging and heat management for High Power Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT) devices is critical due to the rising demand for high-power electronics across various sectors. GaN HEMT devices offer significant advantages including high-power density, efficiency, and frequency operation. However, their operation generates substantial heat that can compromise performance and reliability if not managed effectively. Thus, integrating these GaN HEMT devices into multiple subsystems with advanced packaging techniques and efficient heat management strategies is vital.

AI Image Verification

Purpose: AI transforms electronic packaging assembly lines by automating optical inspection, predicting maintenance needs, and expediting root cause analysis. Automated optical inspection systems with machine learning algorithms detect minute defects, ensuring higher quality. Predictive maintenance reduces downtime by anticipating equipment failures. AI’s adaptive learning continually improves accuracy. Challenges include infrastructure investment and data privacy considerations. This topic aims to achieve the goal of integrating AI to have intelligent assembly lines, enhancing both operational excellence and product quality.

Advanced Packaging Materials

Power Semiconductors