Unintrusive Aging Analysis based on Offline Learning

Slides:

Advertisements

Similar presentations

Introduction to DFT Alexander Gnusin.

Advertisements

Digital Integrated Circuits© Prentice Hall 1995 Design Methodologies Design for Test.

Reliability Enhancement via Sleep Transistors Frank Sill Torres +, Claas Cornelius*, Dirk Timmermann* + Department of Electronic Engineering, Federal University.

V. Vaithianathan, AP/ECE

Discovering and Exploiting Program Phases Timothy Sherwood, Erez Perelman, Greg Hamerly, Suleyman Sair, Brad Calder CSE 231 Presentation by Justin Ma.

Electronic Systems Design Group Department of Electronics and Computer Science University of Southampton, UK Application of Group Delay Equalisation in.

Copyright 2001, Agrawal & BushnellVLSI Test: Lecture 261 Lecture 26 Logic BIST Architectures n Motivation n Built-in Logic Block Observer (BILBO) n Test.

VSE Corporation Proprietary Information

Slides based on Kewal Saluja

MIAMI Medical Instrument Analysis and Machine Intelligence

4/15/2017 Using Gaussian Process Regression for Efficient Motion Planning in Environments with Deformable Objects Barbara Frank, Cyrill Stachniss, Nichola.

2002 Conference & 8th Annual General Meeting Maintenance System with DeviceNet Presented by Tai Tateishi for OMRON Maintenance System with DeviceNet Masaru.

Using Rational Approximations for Evaluating the Reliability of Highly Reliable Systems Z. Koren, J. Rajagopal, C. M. Krishna, and I. Koren Dept. of Elect.

Failure Trends in a Large Disk Drive Population Authors: Eduardo Pinheiro, Wolf- Dietrich Weber and Luiz Andr´e Barroso Presented by Vinuthna & Arjun.

University of Michigan Electrical Engineering and Computer Science University of Michigan Electrical Engineering and Computer Science 1 Self-calibrated.

ELEN 468 Lecture 251 ELEN 468 Advanced Logic Design Lecture 25 Built-in Self Test.

Peer-to-peer archival data trading Brian Cooper and Hector Garcia-Molina Stanford University.

Reliability Analysis of Flexible Electronics: Case Study of Hydrogenated Amorphous Silicon (a-Si:H) TFT Scan Driver Tsung-Ching (Jim) Huang Tim Cheng Feb.

University of Michigan Electrical Engineering and Computer Science University of Michigan Electrical Engineering and Computer Science 1 Maestro: Orchestrating.

Vegard Joa Moseng BI - BL Student meeting Reliability analysis summary for the BLEDP.

Digital Temperature Sensing in a Variable Supply Environment EE241 Term Project Matthew Spencer Steven Callender Spring 2009.

Advanced Computing and Information Systems laboratory Device Variability Impact on Logic Gate Failure Rates Erin Taylor and José Fortes Department of Electrical.

Relex Reliability Software “the intuitive solution!” Relex Software Corporation 1.

Low Power Artificial Retina By Duha Jabakhanji. Objectives Design a subretinal implant. Design a temperature variable supply voltage (TVS). Integrate.

PUT JOSH WEB- STREAM HERE. 4/30/2010 Iowa State University EE492 – Senior Design II.

Nano Technology for Microelectronics Packaging

Testing of integrated circuits and design for testability J. Christiansen CERN - EP/MIC

Daniel J. Abadi · Adam Marcus · Samuel R. Madden ·Kate Hollenbach Presenter: Vishnu Prathish Date: Oct 1 st 2013 CS 848 – Information Integration on the.

Robust Low Power VLSI ECE 7502 S2015 Minimum Supply Voltage and Very- Low-Voltage Testing ECE 7502 Class Discussion Elena Weinberg Thursday, April 16,

J1879 Robustness Validation Hand Book A Joint SAE, ZVEI, JSAE, AEC Automotive Electronics Robustness Validation Plan The current qualification and verification.

9 September 2009 Beam Loss Monitoring with Optical Fibers for Particle Accelerators Joint QUASAR and THz Group Workshop.

Universidade do Minho Escola de Engenharia Techniques for Modeling Discrete Controllers for the Optimization of Hybrid Plants: a Case Study Universidade.

Sill Torres, Bastos: mBBICS Robust Modular Bulk Built-In Current Sensors for Detection of Transient Faults Frank Sill Torres +, Rodrigo Possamai Bastos*

Maeda, Sill Torres: CLEVER CLEVER: Cross-Layer Error Verification Evaluation and Reporting Rafael Kioji Vivas Maeda, Frank Sill Torres Federal University.

Improving Voltage Assignment by Outlier Detection and Incremental Placement Huaizhi Wu* and Martin D.F. Wong** * Atoptech, Inc. ** University of Illinois.

Patricia Gonzalez Divya Akella VLSI Class Project.

CONTENTS: 1.Abstract. 2.Objective. 3.Block diagram. 4.Methodology. 5.Advantages and Disadvantages. 6.Applications. 7.Conclusion.

The new generation of Supervisory System Main Features: MONITORING is based on reading and saving the values from the field and then using these values.

Sill Torres: Pipelined SAR Pipelined SAR with Comparator-Based Switch-Capacitor Residue Amplification Pedro Henrique Köhler Marra Pinto and Frank Sill.

Raghuraman Balasubramanian Karthikeyan Sankaralingam

Maieutic Parataxis.

The Role of Smart Transformers within Microgrids

2014 Spring ASIC/SOC Design

Supervised Learning Based Model for Predicting Variability-Induced Timing Errors Xun Jiao, Abbas Rahimi, Balakrishnan Narayanaswamy, Hamed Fatemi, Jose.

Smart levee monitoring and flood decision support system: reference architecture and urgent computing management Bartosz Baliś, Tomasz Bartynski, Marian.

Georgia Institute of Technology PHM17 Conference

Pedro Henrique Köhler Marra Pinto and Frank Sill Torres

INTERNET OF THINGS Introduction to IoT. INTERNET OF THINGS Introduction to IoT.

Martin Shaw – Reliability Solutions

JMP Helps Describe nth Order Degradation Models for the US Army

VLSI Testing Lecture 14: Built-In Self-Test

Localization by RFID ref:

Maestro: Orchestrating Lifetime Reliability in Chip Multiprocessors

J1879 Robustness Validation Hand Book A Joint SAE, ZVEI, JSAE, AEC Automotive Electronics Robustness Validation Plan Robustness Diagram Trends and Challenges.

How to Build Smart Appliances?

Dual Mode Logic An approach for high speed and energy efficient design

Design of benchmark circuit s5378 for reduced scan mode activity

Frank Sill Torres1,2, Pedro A. Silva3, Geraldo Fontes3, José A. M

ورود اطلاعات بصورت غيربرخط

Post-Silicon Calibration for Large-Volume Products

Sungho Kang Yonsei University

Synchronization of Clocked Field-Coupled Circuits

Testing in the Fourth Dimension

Knowing When to Stop: An Examination of Methods to Minimize the False Negative Risk of Automated Abort Triggers RAM XI Training Summit October 2018 Patrick.

The Vision of Self-Aware Performance Models

The Impact of Aging on FPGAs

Encountering Gate Oxide Breakdown with Shadow Transistors to Increase Reliability Claas Cornelius1, Frank Sill2, Hagen Sämrow1, Jakob Salzmann1, Dirk Timmermann1,

Guihai Yan, Yinhe Han, and Xiaowei Li

Lecture 26 Logic BIST Architectures

HotAging — Impact of Power Dissipation on Hardware Degradation

Presentation transcript:

Unintrusive Aging Analysis based on Offline Learning Frank Sill Torres*+, Pedro Fausto Rodrigues Leite Jr.*, Rolf Drechsler+ *Universidade Federal de Minas Gerias, Belo Horizonte, Brazil +University of Bremen, Bremen, Germany

Motivation Aging of integrated systems of rising importance But: (Still) less critical for customer applications Interest in low weight solutions (S.M.A.R.T. for HDDs, …) This work: Low-weight aging monitoring / remaining lifetime prediction Based on (offline) learning V

Aging Monitoring In-situ slack sensors Detection / preview of failing timing Added invasively to (selected) critical paths Online self-testing Built-In Self-Test (BIST) during test mode Additional circuitry (Scan chains, …) Aging sensors Report experienced aging Ignores system’s activity C

Unintrusive Aging Analysis Architecture VDD, Freq., Sleep Temp, V, Activity APDB, MDB: Databases

Unintrusive Aging Analysis Profiling Sensors Temperature, voltage, activity, … Low area offset, unintrusive Profiling Simulations Aging characterization at design time Various scenarios (Temp, VDD, activity, …) Parameter can vary Also possible: Data from stress test / field

Unintrusive Aging Analysis Compression and Profile Storage Compression of simulated / measured data Insertion in Databases Set 4 Set 3 Sensor Value Set 2 Set 1 Time … Sensor ST,4 MTTF in Set 0 [%] in Set 4 20 % 32 % 2e2 h Data bases for Profile Data (APDB) Measured Data (MDB) MTTF – Mean Time To Failure

Unintrusive Aging Analysis Prediction Models Prediction Relate Measured data (MDB) to Profiling Data (APDB) for prediction of current Remaining Useful Lifetime (RUL) Three Models (Linear, Euclidean Distance, Correlation)

Results Best (Linear): 90.4%

Conclusions Methodology for low weight prediction of aging of integrated systems Application of profiling data Consideration of varying parameters Simulation results: Prediction accuracy ca. 90 % → Not exact but Enables proactive counter measurements User can be warned

Unintrusive Aging Analysis based on Offline Learning ART Thank you! www.asic-reliability.com franksill@ufmg.br

Activity Sensor [7] R. Baranowski, et al., "On-line prediction of NBTI-induced aging rates," in DATE 2015, pp. 589-592. Monitoring of switching activity of the circuit’s primary inputs (PI) or pseudo-primary inputs (PPI)

Aging Altera, RELIABILITY REPORT 56, 2013