The Computing-In-Memory (CIM) paradigm offers a promising solution to the memory-wall bottleneck that limits conventional Von Neumann architectures. By performing data processing at the same physical location where the data are stored, CIM-based architectures minimize costly data movement and drastically improve energy efficiency. When implemented with Ferroelectric Field Effect Transistors (FeFETs), additional advantages from the non-volatility, fast switching, and low operating voltage of FeFETs are added. However, the widespread adoption of FeFETs is limited by their poor endurance, which is overcome by a Back End of the Line (BEoL) integration of FeFET-2, where a ferroelectric capacitor (FeCAP) is wired to the gate of a CMOS transistor providing high endurance compatible with low-power edge applications. These properties enable dense, low-power, and high-speed matrix operations essential for AI workloads. As a result, FeFET-2-based CIM accelerators offer a promising solution for energy-efficient, high-performance AI at the edge. The Ferro4EdgeAI project aims to develop an ultra low-power, scalable edge accelerator for AI, targeting a significant gain in energy efficiency with respect to state-of-the-art AI hardware accelerators. To attain this, our project focuses on innovation all along the value chain from materials, physic concepts, device architecture, integration technologies, and accelerators in a holistic design space exploration approach.

Static Random Access Memories (SRAM) are fast and efficient circuits used as the main working memory of processing units. However, associating these volatile memories with external non-volatile memories leads to energy consumption and area penalties, while leading to security issues. Ferroelectric-based NVSRAMs are one of the most promising ways of combining the high efficiency of SRAMs with non-volatile operations to tackle these challenges. In this work, several design parameters of the bitcell are optimized to ensure error-less data transfer between 6T SRAM internal nodes and 4 ferroelectric capacitors (4C). The presented 6T4C bitcell presents STORE and RECALL energies of 161fJ/bit and 27fJ/bit, respectively, and STORE and RECALL times of 480ns and 245ns, respectively. A high reliability is achieved from −40°C to +85°C for SS, TT and FF fabrication corners. The integration of the four FeCAPs in the bitcell leads to a 46% area overhead, a 94% WRITE time degradation, and a 32% WRITE energy increase. However, an increase of less than 0.5% in both READ time and energy has been observed. A previously developed Fast-Erase system has also been integrated for countering cold-boot attacks. Combining design optimizations and Fast-Erase technique ensures cold-boot attack immunity of the memory and enables error-less RECALL with WRITE operations between STORE and RECALL, leading to new use-cases of NVSRAM circuits.

In an era where embedded systems play an increasingly vital role in critical domains like electric mobility, healthcare, industry, or infrastructure monitoring, the demand for real-time data processing is paramount. This paper addresses the challenges posed by high sensor data rates and limited processing power of microcontrollers (MCUs) in these applications. It introduces a novel computational method leveraging the Serial Ferroelectric RAM (FeRAM) architecture, along with the Computational SRAM concept, and will be called Compute-In-Place (CIP). This exploration of CIP Serial FeRAM reveals its potential for improving predictability, energy efficiency and security in high-throughput processing of large volumes of sensor data. Unlike conventional computing architectures, CIP Serial FeRAM lightens the MCU's computational load, reduces latency and improves energy efficiency by enabling computational tasks within memory. This paper emphasizes the flexibility of CIP Serial FeRAM for diverse real-time tasks, paving the way for more performance, efficient and adaptable critical embedded systems.

Ferroelectric tunneling junctions (FTJ) are considered to be the intrinsically most energy efficient memristors. In this work, specific electrical features of ferroelectric hafnium-zirconium oxide based FTJ devices are investigated. Moreover, the impact on the design of FTJ-based circuits for edge computing applications is discussed by means of two example circuits.