Artificial intelligence (AI) is clearly a transformative force reshaping our technological landscape and igniting a surge of interest in disruptive innovations across all levels of abstraction. As we stand on the brink of an AI revolution, the demand for advanced computing capabilities is skyrocketing. Yet, traditional memory solutions like on-chip SRAM and off-chip DRAM are struggling to keep up, creating a critical bottleneck that can impede the AI juggernaut. Imagine the possibilities if we could achieve over 100X improvements in memory density, bandwidth, latency, performance, and energy efficiency! This isn't just a dream—it's an urgent necessity for the future of AI. Enter ferroelectric devices, which hold incredible potential when co-optimized for key parameter indices (KPIs) across system, design, device, technology and materials. This talk will explore the exciting opportunities and formidable challenges that lie ahead as we transition from established memory devices, technologies and materials to novel ones. Join this journey as we envision a future where memory technology not only supports but accelerates the AI revolution!

Ferroelectric device has been widely investigated as a candidate to replace the charge trap device in NAND flash owing to its low operating voltage, high speed, and structural similarity to conventional cell. Moreover, it has potential to be utilized for the compute-in-memory applications targeting energy-efficient processing of ultra-large AI models. In this talk, the current device-level research progresses on ferroelectric NAND (FeNAND) will be presented briefly, then the array-level NAND cell simulator designed to assess the feasibility of ferroelectric cell will be introduced. Following this, its potential and applicability to compute-in-memory architectures will be discussed.

Perovskite-based Ferroelectric Random Access Memories (FeRAM) cannot scale beyond 130nm and offer poor CMOS compatibility. The discovery of hafnia-zirconia-based films changed FeRAM paradigm about 15 years ago.  This talk will cover HZO-based FeRAM demonstrations from 130nm down to 22nm node, highlighting the opportunities and challenges related to this promising technology.

Ferroelectric tunneling junctions (FTJ) are 2-terminal non-volatile memory devices, that consist of an active ferroelectric layer or multi-layer stack which is sandwiched between two metallic or semiconducting electrodes. In these devices the non-destructive read operation bases on the modulation of the tunneling current by the polarization state of the ferroelectric layer. Due to their high-impedance and rectifying properties FTJs are interesting candidates for the implementation of selector-less passive cross-bar arrays and for massive parallel readout for the realization of MVM in scalable selector-less passive cross-bar arrays. In this talk I will introduce the concept of the FTJ devices and discuss the prospects for their adoption in memory applications and beyond.

Ferroelectric Field-Effect Transistors (FeFETs) are a promising technology with immense potential for in-memory computing and AI acceleration. However, modeling their reliability remains a significant challenge due to multiple sources of variability. Design-time variability from process variations, run-time fluctuations driven by temperature effects, and the inherent stochasticity of ferroelectric domain switching—rooted in its probabilistic nature—make accurate reliability prediction highly complex. Without robust reliability models, it is impossible to ensure the accuracy and dependability of FeFET-based AI accelerator systems, which directly impacts the precision and effectiveness of AI algorithms. This talk presents a holistic framework for reliability estimation, seamlessly integrating insights from device physics to circuit-level analysis. We also highlight the transformative role of deep learning in addressing these challenges, demonstrating how it enables precise reliability modeling and unlocks the full potential of FeFET technology for next-generation computing.

This talk will discuss strategies to implement an accelerated discovery and codesign platform for efficient design and discovery of new ferroelectric materials and their properties in relevant devices. Achieving this goal requires moving beyond conventional, linear approaches to materials discovery, transforming them into a cyclic and iterative process integrating computation, experiment, and theory to formulate the processing-structure-property-performance relationships necessary to advance ferroelectric materials and devices. We will describe our progress in using machine learning to automate and accelerate materials characterization leading to adaptive learning for simulation and high-throughput synthesis and characterization.