A Physics-based Investigation of Pt-salt Doped Carbon Nanotubes for Local Interconnects

We investigate, by combining physical and electrical measurements together with an atomistic-to-circuit modeling approach, the conductance of doped carbon nanotubes (CNTs) and their eligibility as possible candidate for next generation back-end-of-line (BEOL) interconnects. Ab-initio simulations predict a doping-related shift of the Fermi level, which reduces shell chirality variability and improves electrical conductance up to 90% by converting semiconducting shells to metallic. Circuit-level simulations predict up to 88% signal delay improvement with doped vs. pristine CNT. Electrical measurements of Pt-salt doped CNTs provide up to 50% of resistance reduction which is a milestone result for future CNT interconnect technology

Progress on Carbon Nanotube BEOL Interconnects

This article is a review of the current progress and results obtained in the European H2020 CONNECT project. Amongst all the research on carbon nanotube interconnects, those discussed here cover 1) process & growth of carbon nanotube interconnects compatible with back-end-of-line integration, 2) modeling and simulation from atomistic to circuit-level bench-marking and performance prediction, and 3) characterization and electrical measurements. We provide an overview of the current advancements on carbon nanotube interconnects and also regarding the prospects for designing energy efficient integrated circuits. Each selected category is presented in an accessible manner aiming to serve as a review and informative cornerstone on carbon nanotube interconnects

Concentration addition, independent action and generalized concentration addition models for mixture effect prediction of sex hormone synthesis in vitro

Humans are concomitantly exposed to numerous chemicals. An infinite number of combinations and doses thereof can be imagined. For toxicological risk assessment the mathematical prediction of mixture effects, using knowledge on single chemicals, is therefore desirable. We investigated pros and cons of the concentration addition (CA), independent action (IA) and generalized concentration addition (GCA) models. First we measured effects of single chemicals and mixtures thereof on steroid synthesis in H295R cells. Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data. Mixture 1 contained environmental chemicals adjusted in ratio according to human exposure levels. Mixture 2 was a potency adjusted mixture containing five pesticides. Prediction of testosterone effects coincided with the experimental Mixture 1 data. In contrast, antagonism was observed for effects of Mixture 2 on this hormone. The mixtures contained chemicals exerting only limited maximal effects. This hampered prediction by the CA and IA models, whereas the GCA model could be used to predict a full dose response curve. Regarding effects on progesterone and estradiol, some chemicals were having stimulatory effects whereas others had inhibitory effects. The three models were not applicable in this situation and no predictions could be performed. Finally, the expected contributions of single chemicals to the mixture effects were calculated. Prochloraz was the predominant but not sole driver of the mixtures, suggesting that one chemical alone was not responsible for the mixture effects. In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects. A situation with chemicals exerting opposing effects, for which the models could not be applied, was identified. In addition, the data indicate that in non-potency adjusted mixtures the effects cannot always be accounted for by single chemicals