The development of Ferroelectric Field-Effect Transistor (FeFET) manufacturing requires high-quality test solutions, yet research on FeFET testing is still in a nascent stage. To generate a dedicated test method for FeFETs, it is critical to have a deep understanding of manufacturing defects and accurately model them. In this work, we introduce the unique defect, Anomalous Charge Trapping (ACT), in FeFETs. The ACT-defective FeFET is characterized, and the physical mechanism of the defect is explained. Then, we apply the Deviceaware Test (DAT) method to design a specific ACT-defective FeFET model, which includes the physical impact of the defect on the electrical parameters of defect-free models, and calibrate the model with measurement data. Fault modeling is performed based on circuit-level simulations, and dedicated test solutions are proposed.

As emerging non-volatile memory (NVM) devices, Ferroelectric Field-Effect Transistors (FeFETs) present distinctive opportunities for the design of ultra-dense and low-leakage memory systems. For matured FeFET manufacturing, it is extremely important to have an understanding of manufacturing defects and accurately model them to develop effective test solutions. This paper introduces a comprehensive framework for defect and fault modeling, which enables the development of test solutions. First, a classification of FeFET manufacturing defects is provided; both conventional defects (such as contacts and interconnect defects) as well as unique FeFET defects are discussed. The latter FeFET specific defect leads to unique faults that cannot be adequately described using traditional modeling approaches. Then, the Device-Aware Test (DAT) method is used to effectively and appropriately model, analyze and develop test solutions for such unique defects; the approach will be illustrated for Stuck-at-Polarization (SAP) defects.

The mobile element variant is a very important structural variant, accounting for a quarter of structural variants, and it is closely related to many issues such as genetic diseases and species diversity. However, few detection algorithms of mobile element variants have been developed on third-generation sequencing data. We propose an algorithm ricME that combines sequence realignment and identity calculation for detecting mobile element variants. The ricME first performs an initial detection to obtain the positions of insertions and deletions, and extracts the variant sequences; then applies sequence realignment and identity calculation to obtain the transposon classes related to the variant sequences; finally, adopts a multi-level judgment rule to achieve accurate detection of mobile element variants based on the transposon classes and identities. Compared with a representative long-read based mobile element variant detection algorithm rMETL, the ricME improves the F1-score by 11.5 and 21.7% on simulated datasets and real datasets, respectively.