IEEE host events along 2018 are:

All tutorials qualify for credit towards IEEE TTTC certification under the TTEP program. Each tutorial requires a separate registration fee. Attendees of tutorials receive study material, handouts, breakfast and coffee breaks. The study material includes copies of the presentation and bibliographical material, and, when applicable, a relevant textbook (textbooks are provided to attendees who register at IEEE/CS member or non-member rates).

LATS’18 will offer 1 TTEP tutorial of particular interest for industry and academic professionals

Tutorial 1:

Power-Aware Testing in the Era of IoT

Patrick Girard (LIRMM / CRNS), Nicola Nicolici (McMaster University), Xiaoqing Wen (Kyushu Institute of Technology) 

Managing power consumption of circuits and systems is one of the most important challenges for the semiconductor industry in the era of IoT. Power management techniques are used today to control the power dissipation during functional operation. Since the application of these techniques has profound implications on manufacturing test, power-aware testing has become indispensable for low-power LSIs and IoT devices. This tutorial provides a comprehensive and practical coverage of power-aware testing. Its first part gives the background and discusses power issues during test. The second part provides comprehensive information on structural and algorithmic solutions for alleviating test-power-related problems. The third part outlines low-power design techniques and shows how low-power devices can be tested safely without affecting yield and reliability.

VTS’18 will offer 2 TTEP tutorials of particular interest for industry and academic professionals

Tutorial 1 (morning):

Machine Learning and Its Applications in Test

Yu Huang and Gaurav Vega - Mentor, A Siemens Business 

In this tutorial, we will start by covering the basics of machine learning. We will proceed to give a brief overview of the new and exciting field of deep learning. We will show how easy it is to try using machine learning and deep learning, thanks to powerful, free libraries. After offering the required background in machine learning, we will review several important papers in the field of DFT, diagnosis, yield learning, and root cause analysis, which use machine learning algorithms for solving various problems. Finally, we will propose future research directions in the area of testing, where we think machine learning (especially deep learning) can make a big impact.

Tutorial 2 (afternoon):

Learning Techniques for Reliability Monitoring, Mitigation and Adaptation

Mehdi Tahoori, Karlsruhe Institute of Technology 

With increasing the complexity of digital systems and the use of advanced nanoscale technology nodes, various process and runtime variabilities threaten the correct operation of these systems. The interdependence of these reliability detractors and their dependencies to circuit structure as well as running workloads makes it very hard to derive simple deterministic models to analyze and target them. As a result, machine learning techniques can be used to extract useful information which can be used to effectively monitor and improve the reliability of digital systems. These learning schemes are typically performed offline on large data sets in order to obtain various regression models which then are used during runtime operation to predict the health of the system and guide appropriate adaptation and countermeasure schemes.