High-Bandwidth Chiplet Interconnects for Advanced Packaging Technologies in AI/ML Applications: Challenges and Solutions
By Shenggao Li; Mu-Shan Lin; Wei-Chih Chen; Chien-Chun Tsai (TSMC)
The demand for chiplet integration using 2.5D and 3D advanced packaging technologies has surged, driven by the exponential growth in computing performance required by Artificial Intelligence and Machine Learning (AI/ML). This paper reviews these advanced packaging technologies and emphasizes critical design considerations for high-bandwidth chiplet interconnects, which are vital for efficient integration. We address challenges related to bandwidth density, energy efficiency, electromigration, power integrity, and signal integrity. To avoid power overhead, the chiplet interconnect architecture is designed to be as simple as possible, employing a parallel data bus with forwarded clocks. However, achieving high-yield manufacturing and robust performance still necessitates significant efforts in design and technology co-optimization. Despite these challenges, the semiconductor industry is poised for continued growth and innovation, driven by the possibilities unlocked by a robust chiplet ecosystem and novel 3D-IC design methodologies.
To read the full article, click here
Related Chiplet
- Integrated voltage regulator (IVR) chiplet
- High-performance connectivity chiplets
- eFPGA Chiplet
- DPIQ Tx PICs
- IMDD Tx PICs
Related Technical Papers
- Intel Delivers Cutting-Edge Process Technologies to the Data Center with Intel 18A and Advanced Chiplet Packaging
- Defect Analysis and Built-In-Self-Test for Chiplet Interconnects in Fan-out Wafer-Level Packaging
- Material Needs and Measurement Challenges for Advanced Semiconductor Packaging: Understanding the Soft Side of Science
- Wafer Warpage of Silicon Interposer in Manufacturing Processes for High Density 2.5D Advanced Packaging: Causes, Measurement, Analysis and Optimization
Latest Technical Papers
- DICE: Detailed Inter-Chiplet End-to-End PHY Modeling for Accurate Chiplet Simulation
- APEX: an Adaptive Photonic-Electronic Chiplet Interconnection Architecture for DNN Inference
- Formal Foundations for Known Good Reliable Die Screening in Chiplet-Based AI Systems-on-Chip
- Optimization of Test-Access Architectures and Test Scheduling for 2.5D/3D Integration
- Learning to Place Chiplets: A Multi-Objective Reinforcement Learning Approach