In the 2020 symposia of VLSI technology and circuits,IBM Research presented a variety of papers, short courses, workshops and virtual sessions that demonstrated the latest advances in systems research. Their research spotlighted key developments for hybrid cloud infrastructure and AI, marked by improvements in performance, energy efficiency, area scaling, and new workloads.
In their paper, “Improved Air Spacer Co-Integrated with Self-Aligned Contact (SAC) and Contact Over Active Gate (COAG) for Highly Scaled CMOS Technology,”
IBM researchers described how the new air spacer reduces effective capacitance – a critical factor impacting the characteristics of CMOS devices – by 15 percent through a reduction in the air spacer’s dielectric constant, leading to performance gains and power reductions at the same time.
IBM researchers described a hardware demonstration of AI processor core that can be applied to both AI training and inference applications in their paper, “A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference.”
This advancement is part of the Digital AI Core accelerator research in the IBM Research AI Hardware Center. AI hardware accelerators can be used for building and deploying neural network models for applications such as speech recognition, natural language processing and computer vision.
In the paper, “A Monolithically Integrated Silicon Photonics 8×8 Switch in 90nm SOI CMOS,” IBM researchers from the U.S. and Canada presented a silicon photonics-based network switch integrated with switching and control electronics. Silicon photonics, an evolving technology in which optical rays transfer data between computer chips, provides an affordable way to build faster switches. Optical rays can carry far more data in less time than electrical conductors.