Will Chiplet Adoption Mimic IP Adoption?
If we look at the semiconductor industry expansion during the last 25 years, adoption of design IP in every application appears to be one of the major factors of success, with silicon technology incredible development by a x100 factor, from 250nm in 2018 to 3nm (if not 2nm) in 2023. We foresee the move to chiplet-based architecture to soon play the same role that SoC chip-based architecture and massive use of design IP has played in the 2000’s.
The question is how to precisely predict chiplet adoption timeframe and what will be the key enablers for this revolution. We will see if diffusion of innovation theory can be helpful to fine-tune a prediction, determine what type of application will be the driver. Chip-to-chip interconnect protocol standard specifications allowing fast industry adoption, driving applications like IA or smartphone application processor quickly seems to be the top enabler, but EDA tools efficiency or packaging new technologies and dedicated fab creation, among others, are certainly key.
To read the full article, click here
Related Chiplet
- FlexGen Multi-Die Smart Network-on-Chip (NoC) IP
- Ncore Multi-Die Interconnect IP
- Integrated voltage regulator (IVR) chiplet
- High-performance connectivity chiplets
- eFPGA Chiplet
Related Blogs
- The Growing Chiplet Ecosystem: Collaboration, Innovation, and the Next Wave of UCIe Adoption
- Will 2025 Be the Year of the Chiplet?
- Simplifying AI Chip Development: Arm and Synopsys Execs Discuss Chiplet, Subsystem, and IP Integration
- The Future of Chiplet Reliability
Latest Blogs
- SoC and NoC in the Chiplet Era: Understanding Modern SoC Architecture
- From Chiplet Design to Physical Reality: Why AI Hardware Needs Continuous Engineering Intelligence
- Beyond the Die Boundary: How Arteris Multi-Die Technology Is Redefining AI System Design
- Riding the Tide Toward Open Ecosystem-Based Chiplet Integration―Hitachi’s Efforts to Overcome Industry-wide Challenges with Quality Improvement Technology
- 3D IC design for the AI era: an EDA perspective