Ferroelectric random access memory (FeRAM) is a promising candidate to further dynamic random access memory (DRAM) scaling. However, the design of the FeRAM bit cell is nontrivial as the ferroelectric device model is not well supported by EDA tools. Modern integrated circuit design heavily depends on circuit-level SPICE simulators that integrate compact device models through modified nodal analysis (MNA) representation. This paper presents a novel MNA-based SPICE simulation method for ferroelectric device models, targeted at the design space exploration of FeRAM bitcells. Furthermore, this paper provides a co-design procedure for FeRAM bitcells and sense amplifiers via a comprehensive case study.

Abstract Nested named entity recognition (NNER) aims to identify potentially overlapping named entities. Sequence labeling method and span-based method are two commonly used methods in nested named entity recognition. However, the linear structure of sequence labeling method results in relatively poor performance, and span-based method requires traversing all spans, which brings very high time complexity. All of them fail to effectively leverage the positional dependencies between internal and external entities. In order to improve these issues, this paper proposed a nested entity recognition method based on Multidimensional Features and Fuzzy Localization (MFFL). Firstly, this method adopted the shared encoding that fused three features of characters, words, and parts of speech to obtain a multidimensional feature vector representation of the text and obtained rich semantic information in the text. Secondly, we proposed to use the fuzzy localization to assist the model in pinpointing the potential locations of entities. Finally, in the entity classification, it used a window to expand the sub-sequence and enumerate possible candidate entities and predicted the classification labels of these candidate entities. In order to alleviate the problem of error propagation and effectively learn the correlation between fuzzy localization and classification labels, we adopted multi-task learning strategy. This paper conducted several experiments on two public datasets. The experimental results showed that the proposed method achieves ideal results in both nested entity recognition and non-nested entity recognition tasks, and significantly reduced the time complexity of nested entity recognition.