Special session: Hot topics: Statistical test methods

International audienceThe process of testing Integrated Circuits involves a huge amount of data: electrical circuit measurements, information from wafer process monitors, spatial location of the dies, wafer lot numbers, etc. In addition, the relationships between faults, process variations and circuit performance are likely to be very complex and non-linear. Test (and its extension to diagnosis) should be considered as a challenging highly dimensional multivariate problem.Advanced statistical data processing offers a powerful set of tools, borrowed from the fields of data mining, machine learning or artificial intelligence, to get the most out of this data. Indeed, these mathematical tools have opened a number of novel and interesting research lines within the field of IC testing.In this special session, prominent researchers in this field will share their views on this topic and present some of their last findings. The first talk will discuss the interest of likelihood prevalence in random fault simulation. The second talk will show how statistical data analysis can help diagnosing test efficiency. The third talk will deal with the reliability of Alternate Test of AMS-RF circuits. The fourth and last talk will address the idea of mining the test data for improving design manufacturing and even test itself

Genome-wide generation and systematic phenotyping of knockout mice reveals new roles for many genes.

Mutations in whole organisms are powerful ways of interrogating gene function in a realistic context. We describe a program, the Sanger Institute Mouse Genetics Project, that provides a step toward the aim of knocking out all genes and screening each line for a broad range of traits. We found that hitherto unpublished genes were as likely to reveal phenotypes as known genes, suggesting that novel genes represent a rich resource for investigating the molecular basis of disease. We found many unexpected phenotypes detected only because we screened for them, emphasizing the value of screening all mutants for a wide range of traits. Haploinsufficiency and pleiotropy were both surprisingly common. Forty-two percent of genes were essential for viability, and these were less likely to have a paralog and more likely to contribute to a protein complex than other genes. Phenotypic data and more than 900 mutants are openly available for further analysis. PAPERCLIP

Genome-wide association analysis implicates dysregulation of immunity genes in chronic lymphocytic leukaemia

Several chronic lymphocytic leukaemia (CLL) susceptibility loci have been reported; however, much of the heritable risk remains unidentified. Here we perform a meta-analysis of six genome-wide association studies, imputed using a merged reference panel of 1,000 Genomes and UK10K data, totalling 6,200 cases and 17,598 controls after replication. We identify nine risk loci at 1p36.11 (rs34676223, P=5.04 × 10−13), 1q42.13 (rs41271473, P=1.06 × 10−10), 4q24 (rs71597109, P=1.37 × 10−10), 4q35.1 (rs57214277, P=3.69 × 10−8), 6p21.31 (rs3800461, P=1.97 × 10−8), 11q23.2 (rs61904987, P=2.64 × 10−11), 18q21.1 (rs1036935, P=3.27 × 10−8), 19p13.3 (rs7254272, P=4.67 × 10−8) and 22q13.33 (rs140522, P=2.70 × 10−9). These new and established risk loci map to areas of active chromatin and show an over-representation of transcription factor binding for the key determinants of B-cell development and immune response

Genome-wide association analysis implicates dysregulation of immunity genes in chronic lymphocytic leukaemia

Several chronic lymphocytic leukaemia (CLL) susceptibility loci have been reported; however, much of the heritable risk remains unidentified. Here we perform a meta-analysis of six genome-wide association studies, imputed using a merged reference panel of 1,000 Genomes and UK10K data, totalling 6,200 cases and 17,598 controls after replication. We identify nine risk loci at 1p36.11 (rs34676223, P=5.04 × 10−13), 1q42.13 (rs41271473, P=1.06 × 10−10), 4q24 (rs71597109, P=1.37 × 10−10), 4q35.1 (rs57214277, P=3.69 × 10−8), 6p21.31 (rs3800461, P=1.97 × 10−8), 11q23.2 (rs61904987, P=2.64 × 10−11), 18q21.1 (rs1036935, P=3.27 × 10−8), 19p13.3 (rs7254272, P=4.67 × 10−8) and 22q13.33 (rs140522, P=2.70 × 10−9). These new and established risk loci map to areas of active chromatin and show an over-representation of transcription factor binding for the key determinants of B-cell development and immune response

An automated, complete, structural test solution for

Gigahertz serialization and deserialization (SERDES) has become a dominant inter-chip and interboard data transmission technique. Signal integrity is the primary factor determining its bit error rate, typically less than 10-12, so the primary production test challenges are testing picosecond jitter and the signal eye opening. Off-chip jitter and rise/fall time measurements are limited by hardware complexity, access, bandwidth, and noise. Published on-chip measurement techniques are limited by delay line jitter. This paper presents a new jitter test technique that has been demonstrated on an FPGA to achieve less than 1 ps RMS self-jitter, and a new signal eye test that has unlimited bandwidth; neither test uses high speed circuitry. The all-digital technique uses the receiver itself to demodulate the signal jitter to a low-speed bit stream that is analyzed by a single-clock domain, synthesizable circuit. This is combined with logic BIST and 1149.6 boundary scan to completely test an IC

P2 - Can DPPM of AMS Circuits Be Accurately Estimated From Their Defect Coverage?

Estimation of Defective Parts Per Million (DPPM) for digital circuits is no longer a straightforward task for nanotechnologies, even with the help of Williams-Brown or Seth-Agarwal formulas, ATPG and wellestablished fault models. For AMS circuits, the problem has additional dimensions of complexity because defect coverage for different defect models can be obtained only through a defect simulation campaign. For practical reasons, a defect campaign cannot be exhaustive for state-of-the art ICs with AMS modules, implying that at least defect selection, defect likelihoods, and coverage estimate precision will need to be considered among other parameters. Experts from industry and academia will share their views and potentially pave the way to accurate AMS DPPM estimation