The 2024 Conference Program is available here!

2024 Technical Invited Talks

The technical program will be complemented by Invited Presentations from both industry and academia.

• Dr. Sandy Liao, TSMC, Taiwan
  CFET Technology for Future Logic Scaling
• Prof. Saptarshi Das, Penn State U., USA
  Monolithic 3D Integration of Functionally Diverse 2D Devices
• Dr. Kwangmin Park, Samsung, S. Korea
  Pioneering Innovation for Next Generation 3D Memory Era
• Prof. Sumeet Gupta, Purdue U., USA
  Variability in Hafnia-based Ferroelectrics: A Phase-Field Simulation based Perspective
• Dr. Chris Neumann , Intel, USA
  Hafnia-Based FeRAM for High-Density, High-Speed Embedded Memory
• Dr. Adrian Chasin, imec, Belgium
  IGZO Thin-Film Transistor Reliability: the Last Standing Roadblock for Memory Applications
• Prof. Siddharth Rajan, The Ohio State U., USA
  Device Engineering for High-Performance Gallium Oxide Electronics
• Dr. Ruqiang Bao, IBM, USA
  Gate Stack Innovations for Gate-All-Around (GAA) Device Architecture to Continue Transistor Scaling

Invited Honorary Lecture

• Prof. Andre Stesmans, KU Leuven, Belgium
  Electron Spin Resonance as Powerful Spectroscopy for Assessment of Point Defects in Semiconductor/Insulator Structures: Some Historical Reflections on Interfaces

Wednesday Evening Tutorial

The Wed Tutorial will shed light on a single topic in depth, particularly benefiting students and newcomers to the field.

• Prof. Shinichi Takagi, U. Tokyo, Japan
  Hafnia-Based Ferroelectric FETs and Capacitors for Low-Power Memory and AI Applications: Physical Understanding of Device Operation and Reliability

Since the discovery of ferroelectricity in HfO₂-based dielectric films in 2011, ferroelectric devices using HfO₂-based thin films as dielectrics have attracted strong interest. Thus, active research and developments on Si-friendly HfO₂-based ferroelectric FeRAMs and FeFETs have been conducted for memory and logic applications with extremely low power consumption. Furthermore, these HfO₂-based ferroelectric devices are also promising as hardware for realizing AI applications with high energy efficiency, because of the versatile properties such as analog memory characteristics and coexistence of memory and logic functions.

This tutorial will introduce the recent development of FeRAMs and FeFETs for memory and AI applications with an emphasis on the physical understanding of electric characteristics and reliability. For metal/ferroelectric/metal (MFM) capacitors, I will provide the critical issues for FeRAM applications, such as ferroelectric film thickness scaling, operation voltage and reliability including oxide breakdown, wakeup and fatigue. Also, for metal/ferroelectric/semiconductor (MFIS) gate stacks, I will discuss the complicated interaction and coupling between polarization charges, trapped charges and inversion-layer charges, which are of the paramount importance in quantitative understanding of the FeFET operation and the reliability. Based on this knowledge, the memory characteristics and the reliability of FeFETs will be examined from the viewpoint of the ferroelectric film thickness scaling.

The tutorial will also explore the AI applications using the HfO₂-based ferroelectric devices. Various examples of the ferroelectric devices for application to AI computation systems will be introduced. Among them, this presentation will shed light more on physical reservoir computing using FeFETs, which is one of the unique AI computation applications that take advantage of the ferroelectric device properties. Physical reservoir computing, represented by hardware with input-history-dependent and nonlinear dynamics, has recently attracted significant attention as a method to realize real-time AI processing with high energy efficiency at the edge. The principle, the basic AI characteristics and applications to speech recognition of FeFET reservoir computing will be explained.

Past SISC programs are available here.