Dielectric Film Thickness Measurement Via a Convolutional Neural Network for Integrated Circuit Delayering End Point Detection

The integrated circuit (IC) delayering workflow is heavily reliant on operator experience to determine the processing end point, which is the ideal point on an IC where processing should be terminated, to optimize region of interest imaging. The current method of end point detection during IC delayering utilizes qualitative correlation between dielectric film color and dielectric thickness observed via optical microscopy to guide decision making. The goal of this work is to quantify this relationship using computer vision. In the field of computer vision, convolutional neural networks (CNNs) have been successfully applied to capture spatial relationships within images. Given this success, a CNN was trained for thickness estimates of dielectric films using optical images captured during processing for eventual automated end point detection. The trained model explained 39% of the variance in dielectric film thickness with a mean absolute error of approximately 47 nm.

Rapid, Affordable and Scalable SARS-CoV-2 Detection from Saliva

Here we present an inexpensive, rapid, and robust RT-LAMP based SARS-CoV-2 detection method that is easily scalable, enabling point of care facilities and clinical labs to determine results from patients' saliva directly in 30 minutes for less than $2 a sample. The method utilizes a novel combination of widely available reagents that can be prepared in bulk, plated and frozen and remain stable until samples are received. This innovation dramatically reduces preparation time, enabling high-throughput automation and testing with time to results (including setup) in less than one hour for 96 patient samples simultaneously when using a 384 well format. By utilizing a dual-reporter (phenol red pH indicator for end-point detection and SYTO-9 fluorescent dye for real-time), the assay also provides internal validation of results and redundancy in the event of an instrument malfunction.

Rapid Detection of Cytomegalovirus and Listeria monocytogenes Infection in Symptomatic Newborns by Fluorescence End-Point Detection PCR (FEP PCR)

Background: Congenital CMV infection is the most prevalent congenital infection in the world, and perinatal Listeria infection is an important cause of neonatal sepsis. The identification of these two infections is hard. The PCR technique is a sensitive method, which is not simply available in Iran. Objectives: This study investigated the prevalence of CMV and Listeria in 100 urine samples from under 21-day-old neonates with positive risk factors by PCR electrophoresis and fluorescent end-point detection (FEP) PCR in Tehran referral hospitals. Methods: We used 100 urine samples from neonates admitted to 11 medical centers in Tehran with positive selected clinical symptoms compatible with these infections. Electrophoresis and FEP PCR for CMV and Listeria were performed on all urine samples. For the data analysis, SPSS version 21 software was used, and p values of less than 0.05 were considered significant. Results: We collected 100 urine samples from 100 under 21-day-old neonates, referring to Tehran hospitals. Seven cases were positive for Listeria, and 58 cases were positive for CMV by electrophoresis and FEP PCR. Respiratory distress was the most common clinical finding in both groups. The Mc-Nemar test revealed a statistical agreement between calcification and CMV and meningitis and Listeria (P = 0.18 and 0.07, respectively). There was a positive relationship between the neonates’ age and CMV-positive condition (P = 0.001). This result was not significant among Listeria-positive cases. Conclusions: Screening for CMV and Listeria infections in neonates with clinical features compatible with these intrauterine infections may give important information for their treatment and follow-up.