Tobacco streak virus (TSV) ELISA Kit

Non-destructive electrical characterization was performed to detect copper migration in a degraded through-silicon via structure after various stressing conditions, such as elevated temperature exposure, temperature cycling, and electrical biasing. They were performed either independently or as a combination with electrical bias for comparison. Variations in the electrical characteristics reflect the presence of copper. The electrical characteristics were also able to monitor the transport of copper ions from an applied electric field. Physical failure analysis was performed to verify the presence of migrated copper, correlating with the changes observed during electrical measurement. With this understanding, reliability assessments become possible where this paper seeks to value add to verify the influence of Cu migration on the conduction mechanism and time-dependent dielectric breakdown (TDDB) lifetime, in which there is currently a lack in understanding. The conduction mechanism was fitted with experimental data before and after degradation and it was deduced that the Poole-Frenkel conduction mechanism is the dominant mechanism after degradation. However, this is dependent on the copper oxidation state which was verified to change over time from Cu2O to CuO by X-ray photoelectron spectroscopy. TDDB experiments were also performed based on this understanding and found that the presence of copper may accelerate or decelerate time to failure. TDDB lifetime was fitted experimentally and is found to be in good agreement with the root E model. It was verified experimentally by measuring the time to failure at low E-field within reasonable failure time, rather than extrapolating from data at high field.

Generalized Read-Across (GenRA) is a data driven approach that makes read-across predictions on the basis of a similarity weighted activity of source analogues (nearest neighbors). GenRA has been described in more detail in the literature (Shah et al., 2016; Helman et al., 2018). Here we present its implementation within the EPA's CompTox Chemicals Dashboard to provide public access to a GenRA module structured as a read-across workflow. GenRA assists researchers in identifying source analogues, evaluating their validity and making predictions of in vivo toxicity effects for a target substance. Predictions are presented as binary outcomes reflecting the presence or absence of toxicity together with quantitative measures of uncertainty. The approach allows users to identify analogues in different ways, quickly assess the availability of relevant in vivo data for those analogues, and visualize these in a data matrix to evaluate the consistency and concordance of the available experimental data for those analogues before making a GenRA prediction. Predictions can be exported into a tab-separated value (TSV) or Excel file for additional review and analysis (e.g., doses of analogues associated with production of toxic effects). GenRA offers a new capability of making reproducible read-across predictions in an easy-to-use interface.