Awards presented in the 2018 ITC plenary. it includes the 2017 best paper award, Ned Kornfield award, TTTC lifetime contribution award, newly elevated IEEE fellows, Bob Madge Innovation award, and GW Gordon student participation award

2018 ITC offers a comprehensive program on AI, featuring one keynote session, one panel, 14 technical presentations, and 3 TTEP tutorials. 

See the promotion trailer for 2018 ITC AI program. 

We hope this program will facilitate increased dialog about the role of AI in Test during this year’s ITC test week.

 

 

 

AI Keynote Session, Thursday, November 1^st, 11:00-12:00, “AI in Test” by speakers:

• Ken Butler, Founder, Engineering Tools and Analytics Team, Texas Instruments, Dallas, Texas – Semiconductor Test Perspective
• Anne E Gattiker, Principal Research Staff Member, IBM – Deep Learning Perspective
• Ira Leventhal, Vice President, New Concept Product Initiative, Advantest America, Inc. – ATE Perspective
• Xinli Gu, Huawei – System Perspective
• Cheng-Wen Wu, Tsing Hua Distinguished Chair Professor with the EE Dept., NTHU, Hsinchu, Taiwan. – Taiwan Semiconductor Industry Perspective

 AI Panel, Thursday, November 1^st, 13:30-15:00

• “Could AI eliminate the need for test engineering?” Moderated by Rob Aitken

 AI Sessions (8 talks in a two-day AI education track + 6 additional technical presentations)

• Tuesday, October 30

• • • 13:50-14:40: “Safe AI in CPS”
    • 14:40-15:30: “The Mismeasure of AI”
    • 16:00-16:45: “Seeing faces through the eyes of Artificial Intelligence”
    • 16:45-17:30: “Influence-directed explanations for machine learning systems”

• Wednesday, October 31

• • • 08:30-09:15: “Machine Learning for Yield Learning and Optimization”
    • 09:15-10:00: “Practical Applications of Big Data Analytics in Semiconductor Manufacturing, Assembly and Test”
    • 10:30-11:00: “Improving Diagnosis Efficiency via Machine Learning”
    • 11:00-11:30: “Artificial Neural Network Based Test Escape Screening Using Generative Model”
    • 11:30-12:00: “Concept Recognition in Production Yield Data Analytics”
    • 14:00-14:45: “An Autonomous System View To Apply Machine Learning”
    • 14:45-15:30: “Design Automation for Intelligent Automotive Systems”

• Thursday, November 1

• • 09:00-09:30: “AI Engineering Assistants for ATE”
  • 09:30-10:00: “Is it possible to impact quality of test with machine learning?”
  • 10:00-10:30: “Moving Adaptive Test to AI Test”

 TTEP Tutorials

• Sunday Morning, October 28: “Learning Techniques for Reliability Monitoring, Mitigation and Adaptation”
• Sunday Afternoon, October 28: “Machine Learning for Test, and Test for Machine Learning – A Journey to AI”
• Monday Morning, October 29, “From Data to Actions: Applications of Data Analytics in Semiconductor Manufacturing & Test”

 More Information on the Two-day AI Education Track with 8 Talks:

Talk day/time	Speaker name	Email address	Title	Short Description
Tuesday 13:50	Andre Platzer	aplatzer@cs.cmu.edu	Safe AI in CPS	This talk presents provably safe reinforcement learning that provides the best of both worlds: the exploration and optimization capabilities of learning along with the safety guarantees of formal verification.
Tuesday 14:40	Roy Maxion	maxion@cs.cmu.edu	The Mismeasure of AI	This talk will examine sources and consequences of a range of biases, how they influence data, how outcomes based on flawed protocols and hence biased data can be invalid, and how the hazards posed by such invalidities can be mitigated.
Tuesday 16:00	Marios Savvides	marioss@andrew.cmu.edu	Seeing faces through the eyes of Artificial Intelligence	Despite common belief, face detection is not a solved problem especially in tough environments that include crowds and occlusion. Face detection using non-deep learning approaches that meet these challenges will be presented.
Tuesday 16:45	Anupam Datta	danupam@cmu.edu	Influence-directed explanations for machine learning systems	Influence-directed explanations shed light on the inner workings of black-box machine learning systems by identifying components that causally influence system behavior and by providing human-understandable interpretation to the concepts represented by these components. This talk describes instances of this paradigm that are model-agnostic and instances that are specific to deep neural networks.
Wednesday 08:30	David Pan	dpan@ece.utexas.edu	Machine Learning for Yield Learning and Optimization	This talk surveys recent results of using various machine learning/deep learning techniques for performance modeling under uncertainty, lithography modeling with transfer/active learning, lithography hotspot detection, and IC mask optimizations. State-of-the-art methods are explained and challenges/opportunities are discussed.
Wednesday 09:15	Ken Harris	ken.harris@pdf.com	Practical Applications of Big Data Analytics in Semiconductor Manufacturing, Assembly and Test	Traditional data gathering and visualization techniques are becoming less and less useful in today’s manufacturing environment.  In this talk, we review the reasons why, along with practical examples illustrating the importance of integrated, automated analysis and triggered actions in the product ramps of today’s advanced products and manufacturing technologies.
Wednesday14:00	Li-C. Wang	licwang@ece.ucsb.edu	An Autonomous System View To Apply Machine Learning	This talk draws an analogy to the autonomous system view of self-driving car for applying machine learning in a test application. Through such a system view it is more intuitive to see where a particular machine learning approach might be applied and what type of learning problem is to be solved. To give a concrete example, a short demo will be included to illustrate how such an autonomous system can be used for production yield data analytics.
Wednesday 14:45	Qi Zhu	qzhu@northwestern.edu	Design Automation for Intelligent Automotive Systems	This talk will discuss the challenges in designing the next-generation autonomous and connected vehicles, and present promising design automation techniques that tackle these challenges.

We have a set of 31 interesting and diverse posters for you this year.  Posters present a nice information opportunity to discuss various works with the authors.

The poster session will be in the exhibit area on Wednesday from 12:00 – 2:00 PM.

 

 

Poster Listings

[154] Patrick Chen (Intel Corporation, Taipei Taiwan) and James Grealish (Intel Corporation, Hillsboro OR U.S.A). IEEE 1149.1-2013 Intel Product Compliance and Industry Enablement Plans.

Abstract. This poster is to update current Intel product 1149.1-2013 compliance status and plans to enable industry including vendors and customers

 

[155] Michele Portolan (TIMA), Riccardo Cantoro (Politecnico di Torino), Ernest Sanchez (Politecnico di Torino) and Matteo Sonza Reorda (Politecnico di Torino). A Functional Approach to Test and Debug of IEEE 1687 Reconfigurable Networks .

Abstract. IEEE 1687 introduces several novelties, like Reconfigurable Scan Networks, which offer important advantages but can result in extremely complex integrity tests. In this poster we present an innovative functional approach.

 

[156] Thomas Klotz (Hochschule Rosenheim) and Martin Versen (Hochschule Rosenheim). Interpreter of MATLAB/Simulink MAT-Output Files for use as Test and Verification Stimulus for Verilog Simulations with Cadence NCSim.

Abstract. A test circuit is implemented in Verilog with MathWorks’ HDL Coder. An interpreter converts the signal states to a Verilog stimulus. After Cadence import, the design is simulated with NCSim.

 

[157] Kalpana Senthamarai Kannan (Grenoble-INP/TIMA), Michele Portolan (Grenoble-INP/TIMA) and Lorena Anghel (Grenoble-INP/TIMA). Run-Time Aging Prediction Through Machine-Learning .

Abstract. Recent technologies are extremely sensitive to aging, and a-priori margin estimation is difficult and imprecise. The method we propose allies embedded monitors and machine learning to predict aging during runtime.

 

[158] Ernst Wahl (HiTestWare LLC). New Standard Test Interface Language (STIL) Applications.

Abstract. Publication of Standard Test Interface Language (STIL) for Test Flow Specification (IEEE Std. 1450.4-2017) facilitates new applications for the STIL family of standards such as tester-independent IC test program specification.

 

[159] George Lawton Iii (Lawton software llc). Dynamic Cloud-Based Data Collection System.

Abstract. A schema-less cloud-based distributed data collection system with dynamic validation and comprehensive system status and alerting provided a reconfigurable data collection system to solve evolving incoming data schemas.

 

[160] Darshal Patel (AMD) and Khushboo Agarwal (AMD). Layout aware wrapper for IP cores.

Abstract. SOCs are built up of multiple embedded cores with test wrapper around them. This poster describes methodology to use physical location and functional testcase requirements to build wrapper chains.

 

[161] Jeff Rearick (AMD), Al Crouch (Amida), Martin Keim (Mentor, A Siemens Business), Michael Laisne (Dialog Semi) and Glenn Colon-Benet (Intel). IEEE P1687.1: Extending 1687 to non-TAP Interfaces.

Abstract. This poster describes the work of the IEEEP1687.1 Working Group toward developing a standard for accessing on-chip instruments through a 1687 network connected an interface other than the IEEE1149.1 TAP.

 

[162] Anthony Coyette (ON Semiconductor), Ronny Vanhooren (ON Semiconductor), Wim Dobbelaere (ON Semiconductor), Baris Esen (Katholieke Universiteit Leuven), Nektar Xama (Katholieke Universiteit Leuven), Jhon Caicedo (Katholieke Universiteit Leuven) and Georges Gielen (Katholieke Universiteit Leuven). Visually-enhanced Dynamic Part Average Testing.

Abstract. In this work, a method is presented which combines the data from the visual inspection of wafers and the Dynamic Part Average Testing algorithm applied to the specifications.

 

[163] Jungho Kang (Samsung Electro-Mechanics) and Kyungsoo Chae (Samsung Electro-Mechanics). Non-Contact Probing for Electrical Connectivity of Printed Circuits.

Abstract. This study proposes a new non-contact test solution for printed circuit boards. The experiment shows that common circuit pattern defects such as open, short, and via open can be detected.

 

[164] Jim Johnson (SiliconAid Solutions), Alfred Crouch (Amida) and Bill Atwell (SiliconAid Solutions). IJTAG Security.

Abstract. This poster will show several security vulnerabilities and possible ways to mitigate these issues within an IJTAG network. Different levels of security will be shown meet specific device requirements.

 

[165] Jim Johnson (SiliconAid Solutions), Alfred Crouch (Amida) and Bill Atwell (SiliconAid Solutions). IEEE 1687 (IJTAG) Evolution.

Abstract. Poster shows the evolution of IJTAG. How IJTAG fits with other major standards for a total solution. The addition of IEEE 1687.1 and 1687.2 will enhance the solution even more.

 

[166] Rene Krenz-Baath (HSHL). Concurrent IJTAG.

Abstract. In this work we are proposing a novel partitioning concept to a reconfigurable test infrastructure in order to enable an independent operation of different sections of the test infrastructure.

 

[167] Jeffrey Hung (Microsoft) and Vidya Neerkundar (Mentor, A Siemens Business). Are You Really Testing Your Memory?  Automating Test and Verification of All Memory Functionality.

Abstract. The MBIST flow is augmented by leveraging advanced tool features to automate test and verification of both the memory core and peripheral timing/power controls typically ignored by traditional models.

 

[168] Jeff Rearick (AMD), Steve Sunter (Mentor, A Siemens Business) and Vladimir Zivkovic (Cadence). IEEE P1687.2: Extending 1687 to Analog Circuits.

Abstract. This poster describes the status and plans of the IEEE P1687.2 Working Group toward its goal of enabling analog tests to be retargeted via IEEE1687 mechanisms.

 

[169] Karthik Subramanian (Ambarella), Praveen Jaini (Ambarella), Adrian Arozqueta (Mentor, A Siemens Business) and Mohammed Abdelwahid (Mentor, A Siemens Business). In-system and Manufacturing Test Flow for Large Automotive ICs .

Abstract. a DFT architecture and supporting flow addressing the challenges for large automotive chip is proposed based on an industrial flow example including challenges and trade-off to reduce design cycle cost.

 

[170] Xijiang Lin (Mentor, A Siemens Business) and Sudhakar Reddy (University of Iowa). On Generating Fault Diagnosis Patterns in the Presence of Xs.

Abstract. For logic diagnosis, we show that earlier methods may generate invalid tests to distinguish pairs of faults in the presence of unknowns. A method to generate valid tests is proposed.

 

[171] Vidya Achari (Texas Instruments Inc.) and Saminah Chaudhry (Texas Instruments Inc.). Enabling High Quality Silicon with Collaboration of Post Silicon Validation and Systems.

Abstract. Poster on Validating device smartly while improving quality by collaborating with system engineers to define the system-level tests, push validation beyond the limits, define spec compliance and automate datasheet generation.

 

[172] Stacy Ajouri (Texas Instruments) and Dennis Foreman (Texas Instruments). Tester Board tracking system using Streaming RITdb.

Abstract. This poster will present an application using IOT methodology to track and analyze the movement of tester boards as well as provide real time feedback to the test operation.

 

[173] Jon Ferguson (Graphcore), Paul Freeman (Graphcore), Paul Hudson (Mentor, A Siemens Business) and Lee Harrison (Mentor, A Siemens Business). ATE Not Required: A Complete Test Solution to Accelerate First Silicon Shipping of a Large AI Design.

Abstract. We demonstrate a fully automated test execution solution that is centered on SiliconInsight. We brought up all DFT patterns very fast, and were shipping first die samples ahead of schedule.

 

[174] Vidya Neerkundar (Mentor, A Siemens Business) and Ron Press (Mentor, A Siemens Business). Effective Testing of Identical Hierarchical Cores.

Abstract. This poster explains upfront design planning, combined with optimized compression architecture within the hierarchical core(s) that helps benefit from better resource utilization at the chip-level.

 

[175] Tal Kogan (Intel) and Amihay Rabenu (Inetl). Optimal SCAN Vector Count.

Abstract. A novel approach for finding the optimal SCAN configuration is introduced, minimizing the vector count at SoC level. This will lead to minimized overall Test time and Memory footprint.

 

[176] Anne Meixner (The Engineers’ Daughter LLC), Salem Abdennadher (Intel), Stepen Sunter (Mentor, A Siemens Company) and Peter Sarson (Dialog Semiconductor). What’s up with Analog Test Coverage?:  IEEE  P2427 IEEE Working Group Progress.

Abstract. Draft standard has been produced that includes: state-of-the-art in analog fault simulation summary, an extensive set of concise definitions, and rules for clear reporting on analog defect and fault coverage.

 

[177] Marvin Yang (Advantest), Alex Fan (Advantest Inc.) and Ashley Huang (Advantest Inc.). An improvement of routability of memory test interface board  by auto pin assignment algorithm.

Abstract. This paper is to introduce an auto pin assignment algorithm to improve the routability of memory test interface board design.

 

[178] Juan Pulido Sanchez (ON Semiconductor) and Rahul Singhal (Mentor, A Siemens Business). TAP based Scan Testing of High Performance Image Sensor Chip.

Abstract. The poster show an implementation of TAP based scan-testing implementation to standardize DFT pin methodology for On-Semiconductor’s chip. Test coverage of 98.9% stuck-at and 87% at-speed testing was achieved.

 

[179] Hans Martin von Staudt (Dialog Semiconductor). Error Sources to Trim Distributions.

Abstract. This poster approaches the mathematical description of process variation to the trim distributions. The use of self-trim techniques shapes the resulting trimmed distributions significantly but it doesn’t change the width.

 

[180] Kevin Fan (Advantest Taiwan Inc). Discrete-Time Controller Implementation for Automotive High Reliability Testing.

Abstract. This paper specific challenges related to automotive device high reliability requirement,performance evaluation by FVI16 with Digital Feedback Loop capability to assure quality of test and achieve 13% test time improvement.

 

[181] Jackie Cooper (Intel). Method to Measure and Improve Toggle Coverage During High Volume Quick Kill Stress.

Abstract. This poster presents a method to calculate toggle coverage using customization options in Mentor’s commercial ATPG solution and shows how coverage can be improved during stress 10-15%.

 

[182] Rahul Singhal (Mentor, A Siemens Business), Imtiaz Ahmed (Qualcomm Technologies, Inc.) and Subhash Baraiya (Qualcomm India Private Limited). Low Pin Count Testing of an Industry Transceiver Chip.

Abstract. This solution shows low pin count testing implementation of an Industry transceiver chip using Embedded Deterministic Test (EDT)

 

[183] Vidya Achari (Texas Instruments Inc.), Sandeep Achari (Soliton technologies Inc) and Vishnuprasad Narayanan Kutty (soliton technologies inc). Influence of Machine Learning on Post Silicon Validation & Analysis.

Abstract. This poster discusses use of Machine Learning techniques to reduce the engineering effort in analyzing data and Test-time reduction by highlighting the potential risks earlier, derived from previous device data.

 

[184] Hyung Soon Kim (Samsung Electronics), Shin Ho Kang (Samsung Electronics) and Gyu Yeol Kim (Samsung Electronics). Efficient Rising Time Measurement through a Level TPD technique by using TDR Function of Automatic Test Equipment.

Abstract. This paper suggests a new measurement technique dubbed as Level TPD which efficiently measures the rising time of transmission lines, employing Time Domain Reflectometry of Automatic Test Equipment.

 

[185] Michael Laisne (Dialog Semiconductor), Luiz Razera (Dialog Semiconductor), Hans Martin von Staudt (Dialog Semiconductor), Bindhu Vasu (Dialog Semiconductor) and Doru Cioaca (Dialog Semiconductor). Novel IJTAG and IJTAG.1 Architecture Alternatives.

Abstract. This poster presents some novel approaches to IJTAG and IJTAG.1 architectures including an instrument interface capable of controlling the scan chains depending on the instrument state and other unique features.

 

2018 ITC India

ITC India preview in the news and summary of event. Number of registrations has increased significantly from 2017. There are 100+ fellowship attendees (students/faculty from academia, being sponsored by Industry). We have 12 booths from sponsors and exhibits, with exciting demos on display. ITC India will host all attendees for a networking dinner, along with cultural evening.

Conference Highlights

Keynotes from Global Technical Experts in the Test arena

6 Tutorials from Industry/Academia (Day 1)

2 days of Technical Program (8 sessions, 24 papers)

Panel discussion, Demo, Booth/Exhibits and Networking

Sessions on emerging  est needs for Artificial intelligence, Automotive and IoT, Hardware security, System test, Analog and mixed signal test, Yield learning, Test analytics, Test methodology, Benchmarks, Test standards, Memory and 3D test, Diagnosis, DFT architectures, Functional and software-based test.

ITC Is Now Accepting Nominations for the 2018 Gerald W. Gordon Award for Student Volunteerism

The International Test Conference (ITC) , the Test Technology Technical Council and the IEEE Philadelphia Section sponsor the Gerald W. Gordon Award.   The award to the recipient consists of complimentary tutorial registration for one morning and one afternoon tutorial for each of the 2 days they are offered, complimentary full conference registration for ITC, complimentary registration for one of the at conference workshops, up to $750 for travel expenses to the conference and free lodging for the nights of attendance.

Eligibility Requirements

The Gerald W. Gordon Award recipient must be a student in good standing at an accredited university or college. Award recipients must have done volunteer work for one or more IEEE conferences, symposia, workshops and/or organizations dedicated to the development of electronics design and testing fields.

Consideration shall be given to the amount of volunteer service given; the breadth of volunteer service given; the impact of the volunteer service give; and worthiness of the candidate.

Nomination Process

The form for nominating a candidate can be obtained by clicking on the following link, “2018 Gerald W. Gordon Award Nomination form”.  The nominator must complete and submit an application form to summarize why the applicant is qualified for the award and list the relevant service work.  In addition, the nominator must identify 2, but not more than 5 endorsers.  The nominator must inform the endorsers that they need to email an endorsement of the candidate as described on the nomination form.  The nomination form and endorsement emails should be sent to Kenneth Mandl at mandlken@aol.com and Yervant Zorian at Yervant.Zorian@synopsys.com. No later than September 24, 2018.  All candidates will be notified of the selection decision by October 1, 2018.