Back-end of line compatible transistors for hybrid CMOS applications

The low-temperature back-end of line (BEOL) compatible transparent amorphous oxide semiconductor (TAOS) TFTs and poly-Si TFTs are the suitable platforms for three-dimensional (3D) integration hybrid CMOS technologies. The n-channel amorphous indium tungsten oxide (a-IWO) ultra-thin-film transistors (UTFTs) have been successfully fabricated and demonstrated in the category of indium oxide based thin film transistors (TFTs). We have scaled down thickness of a-IWO channel to 4nm. The proposed a-IWO UTFTs with low operation voltages exhibit good electrical characteristics: near ideal subthreshold swing (S.S.) ~ 63mV/dec., high field-effect mobility (FE) ~ 25.3 cm2/V-s. In addition, we also have fabricated the novel less metal contamination Ni-induced lateral crystallization (LC-NILC) p-channel poly-Si TFTs. The matched electrical characteristics of n-channel and p-channel devices with low operation voltage and low IOFF are exhibiting the promising candidate for future hybrid CMOS applications

Measuring Higher-Order Rationality with Belief Control

Determining an individual's strategic reasoning capability based solely on choice data is a complex task. This complexity arises because sophisticated players might have non-equilibrium beliefs about others, leading to non-equilibrium actions. In our study, we pair human participants with computer players known to be fully rational. This use of robot players allows us to disentangle limited reasoning capacity from belief formation and social biases. Our results show that, when paired with robots, subjects consistently demonstrate higher levels of rationality and maintain stable rationality levels across different games compared to when paired with humans. This suggests that strategic reasoning might indeed be a consistent trait in individuals. Furthermore, the identified rationality limits could serve as a measure for evaluating an individual's strategic capacity when their beliefs about others are adequately controlled.Comment: The experimental design and the analysis plan are pre-registered on Open Science Framework (https://osf.io/gye4u/). The experimental instructions can be found at https://mjfong.github.io/SI_MHOR_final.pd

BioRED: A Comprehensive Biomedical Relation Extraction Dataset

Automated relation extraction (RE) from biomedical literature is critical for many downstream text mining applications in both research and real-world settings. However, most existing benchmarking datasets for bio-medical RE only focus on relations of a single type (e.g., protein-protein interactions) at the sentence level, greatly limiting the development of RE systems in biomedicine. In this work, we first review commonly used named entity recognition (NER) and RE datasets. Then we present BioRED, a first-of-its-kind biomedical RE corpus with multiple entity types (e.g., gene/protein, disease, chemical) and relation pairs (e.g., gene-disease; chemical-chemical), on a set of 600 PubMed articles. Further, we label each relation as describing either a novel finding or previously known background knowledge, enabling automated algorithms to differentiate between novel and background information. We assess the utility of BioRED by benchmarking several existing state-of-the-art methods, including BERT-based models, on the NER and RE tasks. Our results show that while existing approaches can reach high performance on the NER task (F-score of 89.3%), there is much room for improvement for the RE task, especially when extracting novel relations (F-score of 47.7%). Our experiments also demonstrate that such a comprehensive dataset can successfully facilitate the development of more accurate, efficient, and robust RE systems for biomedicine

Ultrasmall all-optical plasmonic switch and its application to superresolution imaging

Because of their exceptional local-field enhancement and ultrasmall mode volume, plasmonic components can integrate photonics and electronics at nanoscale, and active control of plasmons is the key. However, all-optical modulation of plasmonic response with nanometer mode volume and unity modulation depth is still lacking. Here we show that scattering from a plasmonic nanoparticle, whose volume is smaller than 0.001 μm3, can be optically switched off with less than 100 μW power. Over 80% modulation depth is observed, and shows no degradation after repetitive switching. The spectral bandwidth approaches 100 nm. The underlying mechanism is suggested to be photothermal effects, and the effective single-particle nonlinearity reaches nearly 10−9 m2/W, which is to our knowledge the largest record of metallic materials to date. As a novel application, the non-bleaching and unlimitedly switchable scattering is used to enhance optical resolution to λ/5 (λ/9 after deconvolution), with 100-fold less intensity requirement compared to similar superresolution techniques. Our work not only opens up a new field of ultrasmall all-optical control based on scattering from a single nanoparticle, but also facilitates superresolution imaging for long-term observation

TAG: Learning Circuit Spatial Embedding From Layouts

Analog and mixed-signal (AMS) circuit designs still rely on human design expertise. Machine learning has been assisting circuit design automation by replacing human experience with artificial intelligence. This paper presents TAG, a new paradigm of learning the circuit representation from layouts leveraging text, self-attention and graph. The embedding network model learns spatial information without manual labeling. We introduce text embedding and a self-attention mechanism to AMS circuit learning. Experimental results demonstrate the ability to predict layout distances between instances with industrial FinFET technology benchmarks. The effectiveness of the circuit representation is verified by showing the transferability to three other learning tasks with limited data in the case studies: layout matching prediction, wirelength estimation, and net parasitic capacitance prediction.Comment: Accepted by ICCAD 202