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2011 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.
          2011 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.




                  International Transaction Journal of Engineering,
                  Management, & Applied Sciences & Technologies
                             http://www.TuEngr.com,         http://go.to/Research




                    Industry Supported Semiconductor Test Engineering
                    Academic Survey and Round-Table at Texas A&M
                    University
                                     *
                    Tom Munnsa , Rainer Finkb, Ellen Onderkoc

a
  Consultant, Electronics Engineering Technology (EET), Texas A&M University
b
  Faculty, Electronics Engineering Technology (EET), Texas A&M University
c
  Program Manager, Semiconductor Test (EET), Texas A&M University


ARTICLEINFO                      A B S T RA C T
Article history:                         This paper describes the research and process involved in
Received 23 August 2011
Accepted 30 September 2011       validating the academic relevance of a University level
Available online                 curriculum in Semiconductor Test. Texas A&M University
25 December 2011                 Electronics Engineering Technology (EET) Program, within the
Keywords:
Texas A&M,
                                 Dwight Look College of Engineering, has a world class Test lab.
Semiconductor,                   This lab, supported by Texas Instruments and Teradyne Inc., has
Digital Test,                    been teaching Mixed Signal test at the undergraduate level for
Curriculum,
Industry-based survey.
                                 over 12 years. The Lab faculty and staff were interested in the
                                 technical relevance of their curriculum and engaged an Industry
                                 standards organization to co-sponsor an industry-based survey.
                                 SEMI’s Collaboration of Automated Semiconductor Test
                                 (CAST) was chosen and agreed. This survey polled engineers,
                                 managers, and professionals within the semiconductor industry
                                 with Test Engineering questions, which revealed specific
                                 feedback on what they would like college new hires to know
                                 before reporting to work. Feedback and results from 144 mostly
                                 senior level Industry colleagues are summarized.


                                   2011 International Transaction Journal of Engineering, Management, &
                                 Applied Sciences & Technologies.




*Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu,
onderko@tamu.edu.       2011. International    Transaction    Journal  of   Engineering,
Management, & Applied Sciences & Technologies.             Volume 2 No.5 (Special Issue).              531
ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf
1. Introduction 
    A major tenet of any physical system or engineering design is to keep the system under
control with some kind of feedback or control loop. The same philosophy can be used to
monitor and improve other less physical systems in our environment. This basic engineering
principle was the driving force behind a closed loop review and monitoring of the
Semiconductor Test industry employment skill needs, and the Test Engineering curriculum
output at Texas A&M University. This closed loop review resulted in a complex and detailed
data intensive result. We decided to break the loop into two topics. This paper reflects the first
topic: the research and process involved with soliciting industry feedback and validating it.
    A second paper will take the validated data from industry and correlate it with the Texas
A&M University/ETID Electronics Engineering Technology curriculum, and seek validation
of the curriculum with industry needs.
    Texas A&M University chose to partner with the standards body called CAST
(Collaboration of Automated Semiconductor Test) under the organization of SEMI.              The
industry based survey, created and co-sponsored by these two organizations polled engineers,
managers, and professionals within the semiconductor industry. Industry was to provide
specific feedback on what they would like Test Engineering new hires to have learned before
reporting to work.


2. Survey Commissioned 
    To provide the broad Industry feedback necessary for a reliable survey and study, we
needed to partner with an organization that had the infrastructure, and similar educational
interests to support our goals. We were delighted to work with Paul Roddy, chairman of
CAST, operating under the SEMI standard’s organization. Paul, with the Texas A&M Mixed
Signal Test lab staff, authored a set of five polling questions. These questions were reviewed
several times for understanding and purpose. One review cycle even included select members
of the CAST membership. Once the questions were optimized, the survey was web posted for
availability to full CAST membership. We were pleased to have 144 responses, especially
after review of the credentials of the respondents. Thirty replies came from members with
titles of either Director or VP. Thirteen were from company Presidents. Twenty-one claimed
a position of manager, twenty-nine listed Engineer within title, and three were listed with the
prestigious title of Fellow. Within the same population of 144 responders, a group of 20 were
also identified as working directly for the hardware vendors that supply the capital equipment

    532          Tom Munns, Rainer Fink, and Ellen Onderko
for the ATE market. For a deeper understanding of the polled responses, statistics were
calculated separately and tallied uniquely for this subset of the responder’s population.

2.1 Survey Analysis 
     To help understand the significance and validity of the survey itself, the survey was
presented at the Industry and Academia Roundtable (IAR) at Texas A&M University in May
2010. The process reviewed the survey questions, discussed the raw responses, debated the
analysis and condensation of the data, and approved the final summary statements derived
from the survey. Thirty-one test professionals and test aware faculty were in attendance at the
May 2010 IAR. The Roundtable had a total attendance/representation of nine companies and
seven universities. The survey questions polled responses from the topics of programming
languages, test tools, test concepts, and repertoire of DUTs (Devices Under Test) experiences.

2.2 Survey Analysis Methodology 
     The questions of the survey were written in such a way as to gain a relative priority of the
reported answers. We asked for the responders to rank each answer with a priority number.
The first survey question is copied below as an example of the style and wording used in the
survey. The question opens the topic, and then a list of answers is supplied to gather the
technical significance and priority of each.
Example survey question (question number 1)
       If you had a choice of the tools you would like your college new hires to already be
       conversant with – what would those tools be? (Select and rank the top 5.)

  __   PC board design rules and constraints
  __   Software for test development (Identify type: IG-XL, etc.)
  __   Software for validation and analysis
  __   CAD tools overview
  __   Testers and automated test methods
  __   Bench tools and basic electronic measurements (Scopes, meters, signal generators)
  __   Other

     Survey questions 1, 2, and 5 have between seven and eight answers, but we requested the
responders to prioritize only the top five with a number between 1 and 5. Questions 3 and 4
have many polling answers, so we requested a priority of only the top ten. We expected a
value that rated that particular answer as a number between one and ten (for questions 3 and
4). This was a benefit to the understanding of the survey, but did cause some extra

*Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu,
onderko@tamu.edu.       2011. International    Transaction    Journal  of   Engineering,
Management, & Applied Sciences & Technologies.             Volume 2 No.5 (Special Issue).   533
ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf
complication in the data condensation.

                     The result of each question’s data condensation is a detailed chart that summarizes
several complementing methods of data analysis. To help with explanation of these methods,
question 1’s summary chart is copied below as an example (see Figure 1). A brief description
of the calculation and analysis methods will follow.

                                                           Q1: General new hire tools,

                                      160                                                                        1.00




                                                                                                                        avg rank
  respondent count




                                      140

                                                                                                                 1.50
                                      120


                                      100
                                                                                                                 2.00


                                          80                                                                                       count totals
                                                                                                                                   count (1,2,or 3)
                                                                                                                 2.50              rank avg
                                          60


                                          40
                                                                                                                 3.00

                                          20


                                           0                                                                     3.50
                                                          Software Software    CAD     ATE &
                                               PC board                                         Bench
                                                           for test   for      tools    test             Other
                                                design                                          tools
                                                             dev    analysis overview methods
                           count totals          114        118      122      118      139       133      25
                           count (1,2,or 3)     72.00      82.00    77.00    80.00     95.00    100.00   18.00
                           rank avg              2.89      2.77      2.98     2.94     2.56      2.32    2.76



                                                        Figure 1: Example of Summary Chart.


The four analysis methods:
          1.           Chart total respondents count (respondent cared to give any vote). (population 144)
          2.           Chart respondent’s count that were either a 1, 2, or 3 (top priorities).
          3.           Chart the average score (rank or priority) of each answer. (=sum /count)
          4.           Chart the average score (rank or priority) of ATE vendor professionals only.
                       ( =sum/count of ATE population 20)

                     We then charted the top 3 selections of each analysis method using black dropping
arrows in order to report the overall weighted score (using overlap of all 4 methods listed
above). In general, the 4 methods of summarizing the raw data were used to help validate the
data itself. In nearly every case, that is for all 5 questions, the summary trend using any one
of the analysis methods was very similar.                                            The dropdown arrows that mark the top 3
selections from each analysis method for question #1 is shown in Figure 2 on a following

                     534              Tom Munns, Rainer Fink, and Ellen Onderko
page. In general this visual mapping overlap tool did help to make the IAR audience more
comfortable with the overall consensus of the data.

2.3 Detailed Methodology using Question 1 as example 
     The first calculation within the summarization of the polled data was to sum up the
answers that were given any vote at all, no matter the priority. If the respondent took the
trouble to give the answer any numerical vote, that answer was tallied. Looking at the
summary chart for question #1 (Figure 2), notice the top line (blue) on the chart that has a
total count of 139 for the fifth answer annotated as “ATE & test methods”. This answer was
the top selection using this analysis method. From the population of 144 respondents, 139
selected this answer with some level of priority. The second highest choice using this total
count method is answer six, “Bench Tools”, with 133 votes, and the third highest selection
was “Software for Analysis” with 122 votes. Figure 2 also has the count of answers that
were voted with a priority ranking of at least a 3, 2, or 1. The second graphed line from the
top (yellow) has a max or high point of 100 on the sixth answer annotated as “Bench Tools”.
One hundred of the 144 respondents selected answer six within their top three priorities.
Second choice using this method of only counting the vote if the priority was 3, 2, or 1 was
answer five “ATE & Test methods” with 95 votes, and third place with 82 votes was answer
2, “Software for Test Development”.

     The next two graphed responses (purple and light blue) are attempts to summarize the
relative priority value of the answers that were reported on the survey. These lines are
mapped to the right axis, and are the calculation of “average” of all the values that were
reported for the element. Because there are 7 answers for the first question, the priority could
be a number between 1 and 7 (however we requested only a ranking of 1 through 5). The
calculation of “average” was accomplished with a simple sum of all numerical values entered,
divided by the count of responders that entered any value for that answer.                  By design the
respondent that did not annotate any response for a particular answer did not influence the
numerator or denominator of the SUM/COUNT equation. The last charted line is similar to
the average rank calculation, but uses data only from the subset of the 20 voting members that
were denoted as working for an ATE vendor company.

     The survey questions will be copied into this report in a very similar format and style as
they were listed on the actual survey website. The final numeric priority given to each answer

*Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu,
onderko@tamu.edu.       2011. International    Transaction    Journal  of   Engineering,
Management, & Applied Sciences & Technologies.             Volume 2 No.5 (Special Issue).        535
ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf
is listed in red font to speed the readability for the interested reader. Of course, the red
annotation was not available at the time of original survey posting. Following the survey
question with red font annotating overall rank order is the detailed chart with responses and
analytical summary per question.


3. Industry  Survey  of  New  Hire  Test  Engineering  Technology 
    Questions and results 
    On the followings you will see the questions and results as returned from the survey.
Question 1:
     If you had a choice of the tools you would like your college new hires to already be
     conversant with – what would those tools be? (Select and rank the top 5.)

     5    PC board design rules and constraints
     3    Software for test development (Identify type: IG-XL, etc.)
     4    Software for validation and analysis
     6    CAD tools overview
     1    Testers and automated test methods
     1    Bench tools and basic electronic measurements (Scopes, meters, signal generators)
          Other


                                                      Q1: General new hire tools,

                                      160                                                                  1
                   respondent count




                                                                                                                 avg rank




                                      140

                                                                                                           1.5
                                      120


                                      100
                                                                                                           2
                                                                                                                            count totals
                                      80                                                                                    count (1,2,or 3)
                                                                                                                            rank avg
                                                                                                           2.5              rank avg ATE
                                      60


                                      40
                                                                                                           3

                                      20


                                       0                                                                   3.5
                                                     Software Software    CAD     ATE &
                                            PC board                                       Bench
                                                      for test   for      tools    test            Other
                                             design                                        tools
                                                        dev    analysis overview methods
            count totals                      114      118      122      118      139      133      25
            count (1,2,or 3)                  72       82       77       80       95       100      18
            rank avg                        2.88596 2.77119 2.97541 2.94068 2.56115 2.32331        2.76
            rank avg ATE                    3.05882 3.26667    3.375   3.21429 2.94737 2.22222      2




                  Figure 2: Results of question #1, General New Hire Tool set.


    536             Tom Munns, Rainer Fink, and Ellen Onderko
Question 2:
        If you had a choice of the programming languages you would like your college
        new hires to be conversant with – what languages would you choose? (Select and
        rank the top 5)

       2                           C
       1                           C++
       4                           Java
       5                           Perl
       3                           Visual Basic (Identify type: VB, VBA, VBT)
       6                           Ruby ( open-source, multi-paradigm, interpreted programming language)
                                   Other
                                                             Q2: Preferred Language


                                     140                                                               1
                respondent count




                                                                                                             avg rank
                                                                                                       1.5
                                     120


                                                                                                       2
                                     100

                                                                                                       2.5
                                     80                                                                                     count totals
                                                                                                       3                    count (1,2,or 3)
                                                                                                                            rank avg
                                     60                                                                                     rank avg ATE
                                                                                                       3.5
                                                                                                                        green arrow
                                     40
                                                                                                                        4th priority
                                                                                                       4


                                     20
                                                                                                       4.5


                                      0                                                                5
                                                                             Visual
                                              C      C++     Java     Perl            Ruby     Other
                                                                             Basic
           count totals                      100     128     115      101     104      38       17
           count (1,2,or 3)                   68     110      68      56      58       11       12
           rank avg                          2.74   2.03125 3.13043 3.23762 3.08654 4.05263 2.58824
           rank avg ATE                     2.64286 2.05263 3.38462 3.27778 2.66667   4.8        4



                                           Figure 3: Results of question #2, Preferred language.

Question 3:
        If you had a choice of what devices you would like your new hires to have already
        tested, would they include: (Select and rank the top 10).

     ___                           Resistors                            ___                 Capacitors
     _5_                           Operational amplifiers               ___                 Counters
     ___                           Voltage regulators                   ___                 FETs
     ___                           Digital to analog converters         _4_                 Analog to digital converters
     _2_                           Microprocessors and/or general logic _3_                 RF components
     _1_                           Mixed-signal devices                 ___                 Other
*Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu,
onderko@tamu.edu.       2011. International    Transaction    Journal  of   Engineering,
Management, & Applied Sciences & Technologies.             Volume 2 No.5 (Special Issue).                                              537
ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf
Q3: Requested Device test experience
                                  140                                                                                     2



               respondent count




                                                                                                                              avg rank
                                  120
                                                                                                                          3


                                  100
                                                                                                                          4

                                  80

                                                                                                                          5              count totals
                                                                                                                                         count (1,2,or 3)
                                  60
                                                                                                                                         rank avg
                                                                                                                                         rank avg ATE
                                                                                                                          6
                                  40


                                                                                                                          7
                                  20



                                   0                                                                                      8
                                                                Volta          D to   A to   up &
                                        Resist       Op Count                                              Mixed-
                                               Caps              ge     FETs     A     D      gen    RF           Other
                                         ors        amps ers                                               signal
                                                                regs           conv   conv   logic
          count totals                   90   115   127    85   116     116    122    128    122     123    126    13
          count (1,2,or 3)               30    32    41    9     19     28     31     47      59     49     60     6
          rank avg                      6.256 6.748 5.283 6.788 5.776 5.422 4.836 4.773 4.352 4.943 4.23 4.462
          rank avg ATE                  5.875 7.308 5.111 6.429 5.313 5.353 4.789 5.053 4.263 4.684 4.222         6.5




                       Figure 4: Results of question #3, Requested Device test Experience.


Question 4:
       If you had a choice of concepts you would want college new hires to already be
       familiar with – what would they be: (Select and rank the top 10).

     ___Kelvin connections                                                                 ___BIST techniques
     ___Coherent sampling                                                                  ___Test safety
     ___FFT                                                                                _4_Test program development process
     ___RF interconnections                                                                ___Test time reduction
     _2_Analog measurement                                                                 ___Adaptive test
     _3_Instrumentation ranges and accuracies                                              ___The economics and logistics of test
     ___Mechanical measurement concept and
        tolerances"                                                                        ___Quality assurance (AQL)
     ___High speed interconnection                                                         _6_Statistical process control (SPC)
     ___High current interconnection                                                       ___Mechanical       measurement      and
tolerances
     ___Low current interconnection                                                        _5_Project management
     ___Logic Scan testing                                                                 _1_Problem solving techniques
     ___Memory Test Algorithms                                                             ___Other




    538                            Tom Munns, Rainer Fink, and Ellen Onderko
ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf
539      Management, & Applied Sciences & Technologies.             Volume 2 No.5 (Special Issue).
         onderko@tamu.edu.       2011. International    Transaction    Journal  of   Engineering,
         *Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu,
                        Figure 5: Results of question #4, Test Concepts.
                                                                                                respondent count
                           Co
                              he         K el
                                                             0
                                                                        20
                                                                                 40
                                                                                           60
                                                                                                    80
                                                                                                             100
                                                                                                                     120




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                                                  AQ
                                                                                                                           Q4: Test Concepts




                            l m ntro                  L)
             Pr         P         e  as       l  (S
                 ob roje                 to         PC
                   le  m       ct           le r        )
                                     m
                           so          an ance
                              lvi          ag           s
                                  ng            e
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                                                                             6
                                                                                           5
                                                                                                         4
                                                                                                                     3




                                                                    6.5
                                                                                    5.5
                                                                                                  4.5
                                                                                                               3.5


                                                                                                         avg rank




                                                             4th priority
                                                                             rank avg


                                                             green arrow
                                                                             count totals



                                                                             rank avg ATE
                                                                             count (1,2,or 3)
Question 5:
           What other skills would you consider useful? (Select and rank the top 5)

       4                           *Specific Data analysis tool use
       1                           Basic understanding production test – probe/final
       2                           General understanding of device packaging
                                   *Specific Semiconductor Device physics
                                   *Specific Data management tools
       3                           General Networking and automation
                                   General Clean room environment
                                   Other

                                                 Q5: General Knowledge/ understanding


                                    140                                                         1
                respondent count




                                                                                                      avg rank
                                    120                                                         1.5

                                    100                                                         2

                                                                                                                 count totals
                                     80                                                         2.5
                                                                                                                 count (1,2,or 3)
                                     60                                                         3                rank avg
                                                                                                                 rank avg ATE
                                     40                                                         3.5

                                     20                                                         4

                                      0                                                         4.5
                                                                   g



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             Figure 6: Results of question #5, General Knowledge and Understanding.


4. Industry and Academia Roundtable Review 
    Texas A&M wanted to create a meeting or forum for a different kind of technical
exchange between Industry and Academia. The outcome was a conference and gathering in
May of 2010 in College Station on the campus of Texas A&M University by a select group of
Test professionals from both Industry and Academia. The IAR Mission Statement seems a bit
altruistic, but is listed below:

    540                             Tom Munns, Rainer Fink, and Ellen Onderko
“Creating Industry and Academic cooperation, involvement, collaboration,
           and understanding on a continuous basis”


     The key word in the statement is “understanding”. It had become clear that Industry and
Academia were sometimes using the same words with different meanings. The best
description and overall purpose of the conference was to address the type of questions listed
in the IAR Standing Agenda and Challenge Questions (listed below):


    •   ALL: What engineer types (DFT, Product, & Test) can best be produced on the
        undergraduate level and which require graduate level education?
    •   ACADEMICS: What is the difference between EE and ET Departments, goals, etc?
    •   INDUSTRY: What are your challenges in the next 2 to 5 years which the Universities
        should know about?
    •   ACADEMICS: Which, if any, existing University courses could be adapted to meet
        needs not covered elsewhere?
    •   INDUSTRY: What are the key areas of new technology/process/equipment/tools or
        techniques for which education is needed?
    •   ACADEMICS: What University classes simply do not exist to provide a theoretical
        background in these areas of new technology/process/equipment/tools or techniques?
    •   INDUSTRY: How can the Universities gain expertise if they don’t already have it?
    •   ACADEMICS: Which Universities are currently best equipped to produce which type
        of engineer and why? (Test, Product, and Design)

          IAR Conference SPEAKOUT SESSION: Each participant was invited to prepare
     and present a short 5min presentation on one or more of the topics above, or any other
     visionary topic that is expected to be an Industry challenge in the next 2-5 years.

     It would seem easy to focus exclusively on the major gaps that exist between Industry
and Academia. Because of the concern that the conference could be overwhelmed with a
negative tone of only listing deficiencies, the first conference was organized with select
companies, universities, and professionals that could bring the most energy and positive
support to the needed solution space. The exit feedback forms of those attending attest to the
fact that the topics were balanced, and that they had accomplished an overall productive
outcome at addressing the agenda. The all day conference was extended with an invitation to
a casual group dinner.     More evidence into the extent of positive collaboration was that 85%
of the audience continued the communication process well into the dinner activities.


5. Approved Industry Survey Analysis 
     A perfect topic match for the intent of the Roundtable was to challenge the audience with
*Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu,
onderko@tamu.edu.       2011. International    Transaction    Journal  of   Engineering,
Management, & Applied Sciences & Technologies.             Volume 2 No.5 (Special Issue).   541
ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf
the detailed review of the survey. Consequently the same audience was the first benefactor of
the results of the survey on Test Engineering skills requested of new hires. Once again, the
process was to spend quality time to review the wording of each survey question. Then
debate the raw results of the poll and to come to an understanding of what the Industry
consensus was voicing in their votes.

5.1 IAR Validated Summaries of Survey Results (by specific question) 
Q1: General test tool knowledge of new hire:
    Specific tester language training was not a top priority, however useful. The key priority
identified was for hands on skills in debug of technical issues in a lab bench environment
(Oscilloscopes, frequency analyzers, etc…). The IAR review committee debated the survey
results as it pertained to knowledge and acquired skill in use of Automated test Equipment
(ATE), and considered use of the ATE as an extension of the lab bench environment.           The
IAR review made it clear that the aptitude of students in these areas translated to skills usable
and favored in the work environment.          The subsequent skills of CAD design, and in
particular PC board layout were not without relatively high interest and marks, but the top
response was for new hires to enter the work force with basic Bench-top Test and practical
debug skills.


Q2: Preferred language:
    The survey confirmed that the most highly used language within the Semiconductor Test
industry is still C or C++ based. We assumed this was within a UNIX environment, but we
had no statistical means to prove this statement. The specific tester language of the current
Teradyne Mixed Signal test platform (FLEX/microFLEX) was selected in third place (Visual
Basic with VBT extensions). The committee was somewhat surprised that the scripting
language of PERL received only half as many top three votes as the C++ response. We had
assumed that PERL would be treated hand in hand with either program automation or post
tester result data manipulation. The respondents also treated the JAVA response with similar
low relevance, fourth out of six options. We assume this is possibly in deference to the
predominance of the C and C++ languages (assumed to be within the UNIX domain).
However it could be a low level vote for JAVA used in Software testing and WEB site testing
techniques. Though it was not one of the given answers to the survey question, the
respondents did write-in several references to National Instrument’s LABVIEW as a language
skill of new hires. For this chart the visual dropdown arrows were enhanced to include the
top four selections from each of the four analysis methods.          This weighting helped to
illuminate the industry interest in VBT over JAVA.

    542          Tom Munns, Rainer Fink, and Ellen Onderko
Q3: Requested Device Test Experience, Repertoire:
     In general the survey responses point to the industry preference to have students already
trained and experienced with testing complex products. Mixed Signal devices cause an extra
level of complication and challenge. The summary chart for question #3 (Figure 4) reveals
that having only tested a resistor or counter is not enough. The industry is looking for
experience with the most complicated devices possible. The response with the term
microprocessor and general logic had a much larger top 3 response than the question that
listed a simple counter. The selection of a general Mixed Signal device had twice as many
votes as the selection of any individual device (Voltage regulators, op amps, and
DAC/ADC’s). With the combined volume of votes for all of the Mixed Signal responses, the
committee believes a true highest priority from the Industry is knowledge and skill in Mixed
Signal device Test. Memory test was logged heavily in the write-in portion of the responses.


Q4: Test Concepts:
     The responses of this question continue with the strong theme of an Industry need and
want for Analog and Mixed Signal skill in the work place.                  Analog measurement and
instrumentation ranges and accuracies were selected as a strong second and third place
response. It may have extra merit that the top selection for this question was the last selection
in the written list. The respondents banked one of their top 3 votes for the last item in the list,
which was “problem solving techniques”. One selection that did attract more response than
the Texas A&M staff had predicted was “Project Management”. Project Management was
actually the forth priority of selection from both the total count and top-3 count methods of
data analysis.


Q5: General Knowledge and Understanding:
     The summary of question five had industry feedback that was not completely expected.
The responses for basic understanding of production test and general networking and
automation were expected to be the Industry top selections, however we were interested to see
that general understanding of device packaging took a strong second place within the
feedback. From a pure Test Engineering prospective we were surprised at this outcome, but
as we looked back over the industry respondents from the CAST population, we had
extensive representation from executives and upper management, and they must be telling it
like they see it! Semiconductor Device packaging is a complex issue in the market place.
Specific data analysis and data management tools were not a strong selection of the polling

*Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu,
onderko@tamu.edu.       2011. International    Transaction    Journal  of   Engineering,
Management, & Applied Sciences & Technologies.             Volume 2 No.5 (Special Issue).   543
ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf
community. The IAR committee assumed this was because every company has their own
software product or method for this aspect of their business. Hiring for this specific skill or
training is apparently not necessary.


6. Overall Conclusion 
    Academic programs need to be relevant to provide students with an education that
prepares them not just to think, but also to live and work in the real world. Academia cannot
invent relevancy in isolation. Industry is needed to provide the closing of a feedback loop to
Academia just as Academia is needed to provide the education which industry can build upon
in their new hires. It cannot be done without key people in both Industry and Academia
sitting down to understand the complex systems in which each side must work. When Texas
A&M started out to determine the relevancy of their curriculum, an entire process had to be
created. Industry had to assist in gathering data and also in validating the data that was
collected. Academia could do neither in isolation. Now with validated data, Academia is
faced with how to inject these new concepts and ideas, if needed, into existing curriculum or
generating new courses to fill the identified voids.

    A Center of Excellence from the perspective of either the Academic or Industrial side has
to be an intuition where this feedback loop exists as a series of continuous processes. At
Texas A&M we have created the relationship with SEMI’s Collaboration of Automated
Semiconductor Test (CAST) to ensure the validity of our questions and to gain breadth of our
audience. We have invited major industry and academic players to come together in a new
kind of discussion, the Industry Academia Roundtable (IAR). We then asked the IAR as a
group to discuss and validate the Test Engineering Skills survey, the survey analysis, and the
summary of results. With the validated results, the Texas A&M staff will subsequently review
our curriculum and complete a knowledge and task analysis. This analysis will provide a
clear picture of what we are teaching and what areas are not being adequately covered. We
have already introduced many minor knowledge (lectures) and tasks (labs) into existing
curriculum, and are working on introducing more major changes. It is an understatement to
report that these major changes will still need to evolve through the required University
procedures. Details of this curriculum evolution are planned in a follow on paper.


7. Acknowledgements 
    This paper would not have the industry relevance without the interaction and support of
the SEMI CAST organization. The Texas A&M staff would like to acknowledge and thank

    544          Tom Munns, Rainer Fink, and Ellen Onderko
Paul Roddy, and Karl Stuber for their efforts in proposing the survey questions, and
implementing the survey within the standard operation of the CAST membership.


           Tom Munns is a veteran of microprocessor and large SOC debug and development having worked at
           Motorola/Freescale on three generations of products namely 68k CISC, 88k RISC, and PowerPC families.
           Having held positions in Product Engineering, Design, and most recently Test Engineering, Tom has
           professional and technical skill and interest in how DFT and DFM can improve the time to market, product
           quality, and eventual cost of test for high volume high visibility products. Notably, Tom had direct design
           content into one of the first Motorola microprocessors to implement JTAG IEEE1149.1, and also nurtured the
           industry away from broadside memory test and into serial BIST test of embedded memories in the early years of
           PowerPC development. Tom has a BS and MS in EE from Texas A&M University, and is currently consulting
           and working with the TI Mixed Signal test Lab and the Freescale Digital Systems test lab at Texas A&M.
           These labs, within the department of Engineering at TAMU, have been focused on the internal academic
           training and development of undergraduate students learning to use ATE and specifically Teradyne J750 and
           FLEX test equipment.
           Dr Rainer Fink holds PhD in biomedical engineering from Texas A&M University. After finishing his PhD, he
           joined the Department of Engineering Technology at Texas A&M University. Currently he is serving as the
           Director of the Texas Instruments Mixed-Signal Test Laboratory at Texas A&M. He has been involved in the
           steering committee of the Teradyne Users Group (TUG) for the past 7 years as the special interest group chair
           for the Mixed-Signal, Digital and Product Integrations sections of the conference. Dr. Fink is an active
           entrepenuer and is serving as CTO/Partner for several small startup companies, primarily in the medical device
           industry. His research activities include Mixed-Signal testing, analog circuit design and biomedical electronics.

           Ellen Onderko came to Academia to be the Project Manager of Semiconductor Test from 15 years in the ATE
           industry in Silicon Valley. She acts as the lab manager and facilitator for the use of industry level ATE
           equipment (Teradyne FLEX and J750 systems). She was a field applications training engineer at both LTX and
           Teradyne Inc. Prior to that, she was a government certified Instructional Systems Designer and a contractor
           instructor in the Air Force Satellite Control Network (AFSCN). There she trained civilian contractors and
           military members on Satellite command, control, communications and Remote Tracking Station (RTS) politics,
           infrastructure, hardware and software. Retired from the USAF she earned the highest peacetime medal the
           USAF awards, the Meritorious Service Medal. While in the military she worked from Avionics Electronics
           technician to Wing/Base level Training Manager, Helicopter Rescue. Ellen has to her credit degrees in Human
           Relations (MA), Anthropology (BA), and Instructor in Technology (AS) degrees earned while on the job.

Peer Review: This article has been internationally peer-reviewed and accepted for publication
             according to the guidelines given at the journal’s website. Note: This article
             was accepted and presented at IAJC-ASEE International Joint Conference on
             Engineering and Related Technologies sponsored by IAJC, ASEE, and IEEE,
             venue at University of Hartford, USA during April 29-30, 2011.




*Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu,
onderko@tamu.edu.       2011. International    Transaction    Journal  of   Engineering,
Management, & Applied Sciences & Technologies.             Volume 2 No.5 (Special Issue).                       545
ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf

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Industry Supported Semiconductor Test Engineering Academic Survey and Round-Table at Texas A&M University

  • 1. 2011 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. 2011 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies http://www.TuEngr.com, http://go.to/Research Industry Supported Semiconductor Test Engineering Academic Survey and Round-Table at Texas A&M University * Tom Munnsa , Rainer Finkb, Ellen Onderkoc a Consultant, Electronics Engineering Technology (EET), Texas A&M University b Faculty, Electronics Engineering Technology (EET), Texas A&M University c Program Manager, Semiconductor Test (EET), Texas A&M University ARTICLEINFO A B S T RA C T Article history: This paper describes the research and process involved in Received 23 August 2011 Accepted 30 September 2011 validating the academic relevance of a University level Available online curriculum in Semiconductor Test. Texas A&M University 25 December 2011 Electronics Engineering Technology (EET) Program, within the Keywords: Texas A&M, Dwight Look College of Engineering, has a world class Test lab. Semiconductor, This lab, supported by Texas Instruments and Teradyne Inc., has Digital Test, been teaching Mixed Signal test at the undergraduate level for Curriculum, Industry-based survey. over 12 years. The Lab faculty and staff were interested in the technical relevance of their curriculum and engaged an Industry standards organization to co-sponsor an industry-based survey. SEMI’s Collaboration of Automated Semiconductor Test (CAST) was chosen and agreed. This survey polled engineers, managers, and professionals within the semiconductor industry with Test Engineering questions, which revealed specific feedback on what they would like college new hires to know before reporting to work. Feedback and results from 144 mostly senior level Industry colleagues are summarized. 2011 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. *Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu, onderko@tamu.edu. 2011. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 2 No.5 (Special Issue). 531 ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf
  • 2. 1. Introduction  A major tenet of any physical system or engineering design is to keep the system under control with some kind of feedback or control loop. The same philosophy can be used to monitor and improve other less physical systems in our environment. This basic engineering principle was the driving force behind a closed loop review and monitoring of the Semiconductor Test industry employment skill needs, and the Test Engineering curriculum output at Texas A&M University. This closed loop review resulted in a complex and detailed data intensive result. We decided to break the loop into two topics. This paper reflects the first topic: the research and process involved with soliciting industry feedback and validating it. A second paper will take the validated data from industry and correlate it with the Texas A&M University/ETID Electronics Engineering Technology curriculum, and seek validation of the curriculum with industry needs. Texas A&M University chose to partner with the standards body called CAST (Collaboration of Automated Semiconductor Test) under the organization of SEMI. The industry based survey, created and co-sponsored by these two organizations polled engineers, managers, and professionals within the semiconductor industry. Industry was to provide specific feedback on what they would like Test Engineering new hires to have learned before reporting to work. 2. Survey Commissioned  To provide the broad Industry feedback necessary for a reliable survey and study, we needed to partner with an organization that had the infrastructure, and similar educational interests to support our goals. We were delighted to work with Paul Roddy, chairman of CAST, operating under the SEMI standard’s organization. Paul, with the Texas A&M Mixed Signal Test lab staff, authored a set of five polling questions. These questions were reviewed several times for understanding and purpose. One review cycle even included select members of the CAST membership. Once the questions were optimized, the survey was web posted for availability to full CAST membership. We were pleased to have 144 responses, especially after review of the credentials of the respondents. Thirty replies came from members with titles of either Director or VP. Thirteen were from company Presidents. Twenty-one claimed a position of manager, twenty-nine listed Engineer within title, and three were listed with the prestigious title of Fellow. Within the same population of 144 responders, a group of 20 were also identified as working directly for the hardware vendors that supply the capital equipment 532 Tom Munns, Rainer Fink, and Ellen Onderko
  • 3. for the ATE market. For a deeper understanding of the polled responses, statistics were calculated separately and tallied uniquely for this subset of the responder’s population. 2.1 Survey Analysis  To help understand the significance and validity of the survey itself, the survey was presented at the Industry and Academia Roundtable (IAR) at Texas A&M University in May 2010. The process reviewed the survey questions, discussed the raw responses, debated the analysis and condensation of the data, and approved the final summary statements derived from the survey. Thirty-one test professionals and test aware faculty were in attendance at the May 2010 IAR. The Roundtable had a total attendance/representation of nine companies and seven universities. The survey questions polled responses from the topics of programming languages, test tools, test concepts, and repertoire of DUTs (Devices Under Test) experiences. 2.2 Survey Analysis Methodology  The questions of the survey were written in such a way as to gain a relative priority of the reported answers. We asked for the responders to rank each answer with a priority number. The first survey question is copied below as an example of the style and wording used in the survey. The question opens the topic, and then a list of answers is supplied to gather the technical significance and priority of each. Example survey question (question number 1) If you had a choice of the tools you would like your college new hires to already be conversant with – what would those tools be? (Select and rank the top 5.) __ PC board design rules and constraints __ Software for test development (Identify type: IG-XL, etc.) __ Software for validation and analysis __ CAD tools overview __ Testers and automated test methods __ Bench tools and basic electronic measurements (Scopes, meters, signal generators) __ Other Survey questions 1, 2, and 5 have between seven and eight answers, but we requested the responders to prioritize only the top five with a number between 1 and 5. Questions 3 and 4 have many polling answers, so we requested a priority of only the top ten. We expected a value that rated that particular answer as a number between one and ten (for questions 3 and 4). This was a benefit to the understanding of the survey, but did cause some extra *Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu, onderko@tamu.edu. 2011. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 2 No.5 (Special Issue). 533 ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf
  • 4. complication in the data condensation. The result of each question’s data condensation is a detailed chart that summarizes several complementing methods of data analysis. To help with explanation of these methods, question 1’s summary chart is copied below as an example (see Figure 1). A brief description of the calculation and analysis methods will follow. Q1: General new hire tools, 160 1.00 avg rank respondent count 140 1.50 120 100 2.00 80 count totals count (1,2,or 3) 2.50 rank avg 60 40 3.00 20 0 3.50 Software Software CAD ATE & PC board Bench for test for tools test Other design tools dev analysis overview methods count totals 114 118 122 118 139 133 25 count (1,2,or 3) 72.00 82.00 77.00 80.00 95.00 100.00 18.00 rank avg 2.89 2.77 2.98 2.94 2.56 2.32 2.76 Figure 1: Example of Summary Chart. The four analysis methods: 1. Chart total respondents count (respondent cared to give any vote). (population 144) 2. Chart respondent’s count that were either a 1, 2, or 3 (top priorities). 3. Chart the average score (rank or priority) of each answer. (=sum /count) 4. Chart the average score (rank or priority) of ATE vendor professionals only. ( =sum/count of ATE population 20) We then charted the top 3 selections of each analysis method using black dropping arrows in order to report the overall weighted score (using overlap of all 4 methods listed above). In general, the 4 methods of summarizing the raw data were used to help validate the data itself. In nearly every case, that is for all 5 questions, the summary trend using any one of the analysis methods was very similar. The dropdown arrows that mark the top 3 selections from each analysis method for question #1 is shown in Figure 2 on a following 534 Tom Munns, Rainer Fink, and Ellen Onderko
  • 5. page. In general this visual mapping overlap tool did help to make the IAR audience more comfortable with the overall consensus of the data. 2.3 Detailed Methodology using Question 1 as example  The first calculation within the summarization of the polled data was to sum up the answers that were given any vote at all, no matter the priority. If the respondent took the trouble to give the answer any numerical vote, that answer was tallied. Looking at the summary chart for question #1 (Figure 2), notice the top line (blue) on the chart that has a total count of 139 for the fifth answer annotated as “ATE & test methods”. This answer was the top selection using this analysis method. From the population of 144 respondents, 139 selected this answer with some level of priority. The second highest choice using this total count method is answer six, “Bench Tools”, with 133 votes, and the third highest selection was “Software for Analysis” with 122 votes. Figure 2 also has the count of answers that were voted with a priority ranking of at least a 3, 2, or 1. The second graphed line from the top (yellow) has a max or high point of 100 on the sixth answer annotated as “Bench Tools”. One hundred of the 144 respondents selected answer six within their top three priorities. Second choice using this method of only counting the vote if the priority was 3, 2, or 1 was answer five “ATE & Test methods” with 95 votes, and third place with 82 votes was answer 2, “Software for Test Development”. The next two graphed responses (purple and light blue) are attempts to summarize the relative priority value of the answers that were reported on the survey. These lines are mapped to the right axis, and are the calculation of “average” of all the values that were reported for the element. Because there are 7 answers for the first question, the priority could be a number between 1 and 7 (however we requested only a ranking of 1 through 5). The calculation of “average” was accomplished with a simple sum of all numerical values entered, divided by the count of responders that entered any value for that answer. By design the respondent that did not annotate any response for a particular answer did not influence the numerator or denominator of the SUM/COUNT equation. The last charted line is similar to the average rank calculation, but uses data only from the subset of the 20 voting members that were denoted as working for an ATE vendor company. The survey questions will be copied into this report in a very similar format and style as they were listed on the actual survey website. The final numeric priority given to each answer *Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu, onderko@tamu.edu. 2011. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 2 No.5 (Special Issue). 535 ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf
  • 6. is listed in red font to speed the readability for the interested reader. Of course, the red annotation was not available at the time of original survey posting. Following the survey question with red font annotating overall rank order is the detailed chart with responses and analytical summary per question. 3. Industry  Survey  of  New  Hire  Test  Engineering  Technology  Questions and results  On the followings you will see the questions and results as returned from the survey. Question 1: If you had a choice of the tools you would like your college new hires to already be conversant with – what would those tools be? (Select and rank the top 5.) 5 PC board design rules and constraints 3 Software for test development (Identify type: IG-XL, etc.) 4 Software for validation and analysis 6 CAD tools overview 1 Testers and automated test methods 1 Bench tools and basic electronic measurements (Scopes, meters, signal generators) Other Q1: General new hire tools, 160 1 respondent count avg rank 140 1.5 120 100 2 count totals 80 count (1,2,or 3) rank avg 2.5 rank avg ATE 60 40 3 20 0 3.5 Software Software CAD ATE & PC board Bench for test for tools test Other design tools dev analysis overview methods count totals 114 118 122 118 139 133 25 count (1,2,or 3) 72 82 77 80 95 100 18 rank avg 2.88596 2.77119 2.97541 2.94068 2.56115 2.32331 2.76 rank avg ATE 3.05882 3.26667 3.375 3.21429 2.94737 2.22222 2 Figure 2: Results of question #1, General New Hire Tool set. 536 Tom Munns, Rainer Fink, and Ellen Onderko
  • 7. Question 2: If you had a choice of the programming languages you would like your college new hires to be conversant with – what languages would you choose? (Select and rank the top 5) 2 C 1 C++ 4 Java 5 Perl 3 Visual Basic (Identify type: VB, VBA, VBT) 6 Ruby ( open-source, multi-paradigm, interpreted programming language) Other Q2: Preferred Language 140 1 respondent count avg rank 1.5 120 2 100 2.5 80 count totals 3 count (1,2,or 3) rank avg 60 rank avg ATE 3.5 green arrow 40 4th priority 4 20 4.5 0 5 Visual C C++ Java Perl Ruby Other Basic count totals 100 128 115 101 104 38 17 count (1,2,or 3) 68 110 68 56 58 11 12 rank avg 2.74 2.03125 3.13043 3.23762 3.08654 4.05263 2.58824 rank avg ATE 2.64286 2.05263 3.38462 3.27778 2.66667 4.8 4 Figure 3: Results of question #2, Preferred language. Question 3: If you had a choice of what devices you would like your new hires to have already tested, would they include: (Select and rank the top 10). ___ Resistors ___ Capacitors _5_ Operational amplifiers ___ Counters ___ Voltage regulators ___ FETs ___ Digital to analog converters _4_ Analog to digital converters _2_ Microprocessors and/or general logic _3_ RF components _1_ Mixed-signal devices ___ Other *Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu, onderko@tamu.edu. 2011. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 2 No.5 (Special Issue). 537 ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf
  • 8. Q3: Requested Device test experience 140 2 respondent count avg rank 120 3 100 4 80 5 count totals count (1,2,or 3) 60 rank avg rank avg ATE 6 40 7 20 0 8 Volta D to A to up & Resist Op Count Mixed- Caps ge FETs A D gen RF Other ors amps ers signal regs conv conv logic count totals 90 115 127 85 116 116 122 128 122 123 126 13 count (1,2,or 3) 30 32 41 9 19 28 31 47 59 49 60 6 rank avg 6.256 6.748 5.283 6.788 5.776 5.422 4.836 4.773 4.352 4.943 4.23 4.462 rank avg ATE 5.875 7.308 5.111 6.429 5.313 5.353 4.789 5.053 4.263 4.684 4.222 6.5 Figure 4: Results of question #3, Requested Device test Experience. Question 4: If you had a choice of concepts you would want college new hires to already be familiar with – what would they be: (Select and rank the top 10). ___Kelvin connections ___BIST techniques ___Coherent sampling ___Test safety ___FFT _4_Test program development process ___RF interconnections ___Test time reduction _2_Analog measurement ___Adaptive test _3_Instrumentation ranges and accuracies ___The economics and logistics of test ___Mechanical measurement concept and tolerances" ___Quality assurance (AQL) ___High speed interconnection _6_Statistical process control (SPC) ___High current interconnection ___Mechanical measurement and tolerances ___Low current interconnection _5_Project management ___Logic Scan testing _1_Problem solving techniques ___Memory Test Algorithms ___Other 538 Tom Munns, Rainer Fink, and Ellen Onderko
  • 9. ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf 539 Management, & Applied Sciences & Technologies. Volume 2 No.5 (Special Issue). onderko@tamu.edu. 2011. International Transaction Journal of Engineering, *Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu, Figure 5: Results of question #4, Test Concepts. respondent count Co he K el 0 20 40 60 80 100 120 r en vin ts c am on pli ng In R Fc str on FF r n T Me ang An ecti cha es a alog ons Hi n i n d me gh cal m acc Hi s e ur as ac gh peed as t ies c i ol . Lo urre nter eran w nt co ce cu nn s rre inte nt rco ectio int n ne n er M L co cti on em ogic n ne or Sc cti y Te an t on st es Al tin BI ST gorit g Te tec hm st h n s pr og Te ique ra m s t s Te de safe st vp ty e co t im r no e r oces mi ed s cs Ad uc S Q a ti tat ua & log ptiv on ist et ica lity a istic e l s su s o st Me pro ra ft ch ce nc e a n ss ica c o e ( st AQ Q4: Test Concepts l m ntro L) Pr P e as l (S ob roje to PC le m ct le r ) m so an ance lvi ag s ng e tec men h n iq t ue s Ot he r 7 6 5 4 3 6.5 5.5 4.5 3.5 avg rank 4th priority rank avg green arrow count totals rank avg ATE count (1,2,or 3)
  • 10. Question 5: What other skills would you consider useful? (Select and rank the top 5) 4 *Specific Data analysis tool use 1 Basic understanding production test – probe/final 2 General understanding of device packaging *Specific Semiconductor Device physics *Specific Data management tools 3 General Networking and automation General Clean room environment Other Q5: General Knowledge/ understanding 140 1 respondent count avg rank 120 1.5 100 2 count totals 80 2.5 count (1,2,or 3) 60 3 rank avg rank avg ATE 40 3.5 20 4 0 4.5 g s t s n e er al en ic ol in us io fin th ys to ag at nm O ol e/ ph m ck t to en ob ro to pa e vi is em pr au ic en ys ce ev st ag d al vi D an om te an an de or n ng ro m a tio ct of at ki n du a uc g ea D at or in on od D fic w Cl nd ic et pr fic ci al m ta N pe ci er g Se rs al pe in *S en de er nd c *S G ifi un en ta ** ec G rs al Sp de er ** en un G c si Ba Figure 6: Results of question #5, General Knowledge and Understanding. 4. Industry and Academia Roundtable Review  Texas A&M wanted to create a meeting or forum for a different kind of technical exchange between Industry and Academia. The outcome was a conference and gathering in May of 2010 in College Station on the campus of Texas A&M University by a select group of Test professionals from both Industry and Academia. The IAR Mission Statement seems a bit altruistic, but is listed below: 540 Tom Munns, Rainer Fink, and Ellen Onderko
  • 11. “Creating Industry and Academic cooperation, involvement, collaboration, and understanding on a continuous basis” The key word in the statement is “understanding”. It had become clear that Industry and Academia were sometimes using the same words with different meanings. The best description and overall purpose of the conference was to address the type of questions listed in the IAR Standing Agenda and Challenge Questions (listed below): • ALL: What engineer types (DFT, Product, & Test) can best be produced on the undergraduate level and which require graduate level education? • ACADEMICS: What is the difference between EE and ET Departments, goals, etc? • INDUSTRY: What are your challenges in the next 2 to 5 years which the Universities should know about? • ACADEMICS: Which, if any, existing University courses could be adapted to meet needs not covered elsewhere? • INDUSTRY: What are the key areas of new technology/process/equipment/tools or techniques for which education is needed? • ACADEMICS: What University classes simply do not exist to provide a theoretical background in these areas of new technology/process/equipment/tools or techniques? • INDUSTRY: How can the Universities gain expertise if they don’t already have it? • ACADEMICS: Which Universities are currently best equipped to produce which type of engineer and why? (Test, Product, and Design) IAR Conference SPEAKOUT SESSION: Each participant was invited to prepare and present a short 5min presentation on one or more of the topics above, or any other visionary topic that is expected to be an Industry challenge in the next 2-5 years. It would seem easy to focus exclusively on the major gaps that exist between Industry and Academia. Because of the concern that the conference could be overwhelmed with a negative tone of only listing deficiencies, the first conference was organized with select companies, universities, and professionals that could bring the most energy and positive support to the needed solution space. The exit feedback forms of those attending attest to the fact that the topics were balanced, and that they had accomplished an overall productive outcome at addressing the agenda. The all day conference was extended with an invitation to a casual group dinner. More evidence into the extent of positive collaboration was that 85% of the audience continued the communication process well into the dinner activities. 5. Approved Industry Survey Analysis  A perfect topic match for the intent of the Roundtable was to challenge the audience with *Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu, onderko@tamu.edu. 2011. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 2 No.5 (Special Issue). 541 ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf
  • 12. the detailed review of the survey. Consequently the same audience was the first benefactor of the results of the survey on Test Engineering skills requested of new hires. Once again, the process was to spend quality time to review the wording of each survey question. Then debate the raw results of the poll and to come to an understanding of what the Industry consensus was voicing in their votes. 5.1 IAR Validated Summaries of Survey Results (by specific question)  Q1: General test tool knowledge of new hire: Specific tester language training was not a top priority, however useful. The key priority identified was for hands on skills in debug of technical issues in a lab bench environment (Oscilloscopes, frequency analyzers, etc…). The IAR review committee debated the survey results as it pertained to knowledge and acquired skill in use of Automated test Equipment (ATE), and considered use of the ATE as an extension of the lab bench environment. The IAR review made it clear that the aptitude of students in these areas translated to skills usable and favored in the work environment. The subsequent skills of CAD design, and in particular PC board layout were not without relatively high interest and marks, but the top response was for new hires to enter the work force with basic Bench-top Test and practical debug skills. Q2: Preferred language: The survey confirmed that the most highly used language within the Semiconductor Test industry is still C or C++ based. We assumed this was within a UNIX environment, but we had no statistical means to prove this statement. The specific tester language of the current Teradyne Mixed Signal test platform (FLEX/microFLEX) was selected in third place (Visual Basic with VBT extensions). The committee was somewhat surprised that the scripting language of PERL received only half as many top three votes as the C++ response. We had assumed that PERL would be treated hand in hand with either program automation or post tester result data manipulation. The respondents also treated the JAVA response with similar low relevance, fourth out of six options. We assume this is possibly in deference to the predominance of the C and C++ languages (assumed to be within the UNIX domain). However it could be a low level vote for JAVA used in Software testing and WEB site testing techniques. Though it was not one of the given answers to the survey question, the respondents did write-in several references to National Instrument’s LABVIEW as a language skill of new hires. For this chart the visual dropdown arrows were enhanced to include the top four selections from each of the four analysis methods. This weighting helped to illuminate the industry interest in VBT over JAVA. 542 Tom Munns, Rainer Fink, and Ellen Onderko
  • 13. Q3: Requested Device Test Experience, Repertoire: In general the survey responses point to the industry preference to have students already trained and experienced with testing complex products. Mixed Signal devices cause an extra level of complication and challenge. The summary chart for question #3 (Figure 4) reveals that having only tested a resistor or counter is not enough. The industry is looking for experience with the most complicated devices possible. The response with the term microprocessor and general logic had a much larger top 3 response than the question that listed a simple counter. The selection of a general Mixed Signal device had twice as many votes as the selection of any individual device (Voltage regulators, op amps, and DAC/ADC’s). With the combined volume of votes for all of the Mixed Signal responses, the committee believes a true highest priority from the Industry is knowledge and skill in Mixed Signal device Test. Memory test was logged heavily in the write-in portion of the responses. Q4: Test Concepts: The responses of this question continue with the strong theme of an Industry need and want for Analog and Mixed Signal skill in the work place. Analog measurement and instrumentation ranges and accuracies were selected as a strong second and third place response. It may have extra merit that the top selection for this question was the last selection in the written list. The respondents banked one of their top 3 votes for the last item in the list, which was “problem solving techniques”. One selection that did attract more response than the Texas A&M staff had predicted was “Project Management”. Project Management was actually the forth priority of selection from both the total count and top-3 count methods of data analysis. Q5: General Knowledge and Understanding: The summary of question five had industry feedback that was not completely expected. The responses for basic understanding of production test and general networking and automation were expected to be the Industry top selections, however we were interested to see that general understanding of device packaging took a strong second place within the feedback. From a pure Test Engineering prospective we were surprised at this outcome, but as we looked back over the industry respondents from the CAST population, we had extensive representation from executives and upper management, and they must be telling it like they see it! Semiconductor Device packaging is a complex issue in the market place. Specific data analysis and data management tools were not a strong selection of the polling *Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu, onderko@tamu.edu. 2011. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 2 No.5 (Special Issue). 543 ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf
  • 14. community. The IAR committee assumed this was because every company has their own software product or method for this aspect of their business. Hiring for this specific skill or training is apparently not necessary. 6. Overall Conclusion  Academic programs need to be relevant to provide students with an education that prepares them not just to think, but also to live and work in the real world. Academia cannot invent relevancy in isolation. Industry is needed to provide the closing of a feedback loop to Academia just as Academia is needed to provide the education which industry can build upon in their new hires. It cannot be done without key people in both Industry and Academia sitting down to understand the complex systems in which each side must work. When Texas A&M started out to determine the relevancy of their curriculum, an entire process had to be created. Industry had to assist in gathering data and also in validating the data that was collected. Academia could do neither in isolation. Now with validated data, Academia is faced with how to inject these new concepts and ideas, if needed, into existing curriculum or generating new courses to fill the identified voids. A Center of Excellence from the perspective of either the Academic or Industrial side has to be an intuition where this feedback loop exists as a series of continuous processes. At Texas A&M we have created the relationship with SEMI’s Collaboration of Automated Semiconductor Test (CAST) to ensure the validity of our questions and to gain breadth of our audience. We have invited major industry and academic players to come together in a new kind of discussion, the Industry Academia Roundtable (IAR). We then asked the IAR as a group to discuss and validate the Test Engineering Skills survey, the survey analysis, and the summary of results. With the validated results, the Texas A&M staff will subsequently review our curriculum and complete a knowledge and task analysis. This analysis will provide a clear picture of what we are teaching and what areas are not being adequately covered. We have already introduced many minor knowledge (lectures) and tasks (labs) into existing curriculum, and are working on introducing more major changes. It is an understatement to report that these major changes will still need to evolve through the required University procedures. Details of this curriculum evolution are planned in a follow on paper. 7. Acknowledgements  This paper would not have the industry relevance without the interaction and support of the SEMI CAST organization. The Texas A&M staff would like to acknowledge and thank 544 Tom Munns, Rainer Fink, and Ellen Onderko
  • 15. Paul Roddy, and Karl Stuber for their efforts in proposing the survey questions, and implementing the survey within the standard operation of the CAST membership. Tom Munns is a veteran of microprocessor and large SOC debug and development having worked at Motorola/Freescale on three generations of products namely 68k CISC, 88k RISC, and PowerPC families. Having held positions in Product Engineering, Design, and most recently Test Engineering, Tom has professional and technical skill and interest in how DFT and DFM can improve the time to market, product quality, and eventual cost of test for high volume high visibility products. Notably, Tom had direct design content into one of the first Motorola microprocessors to implement JTAG IEEE1149.1, and also nurtured the industry away from broadside memory test and into serial BIST test of embedded memories in the early years of PowerPC development. Tom has a BS and MS in EE from Texas A&M University, and is currently consulting and working with the TI Mixed Signal test Lab and the Freescale Digital Systems test lab at Texas A&M. These labs, within the department of Engineering at TAMU, have been focused on the internal academic training and development of undergraduate students learning to use ATE and specifically Teradyne J750 and FLEX test equipment. Dr Rainer Fink holds PhD in biomedical engineering from Texas A&M University. After finishing his PhD, he joined the Department of Engineering Technology at Texas A&M University. Currently he is serving as the Director of the Texas Instruments Mixed-Signal Test Laboratory at Texas A&M. He has been involved in the steering committee of the Teradyne Users Group (TUG) for the past 7 years as the special interest group chair for the Mixed-Signal, Digital and Product Integrations sections of the conference. Dr. Fink is an active entrepenuer and is serving as CTO/Partner for several small startup companies, primarily in the medical device industry. His research activities include Mixed-Signal testing, analog circuit design and biomedical electronics. Ellen Onderko came to Academia to be the Project Manager of Semiconductor Test from 15 years in the ATE industry in Silicon Valley. She acts as the lab manager and facilitator for the use of industry level ATE equipment (Teradyne FLEX and J750 systems). She was a field applications training engineer at both LTX and Teradyne Inc. Prior to that, she was a government certified Instructional Systems Designer and a contractor instructor in the Air Force Satellite Control Network (AFSCN). There she trained civilian contractors and military members on Satellite command, control, communications and Remote Tracking Station (RTS) politics, infrastructure, hardware and software. Retired from the USAF she earned the highest peacetime medal the USAF awards, the Meritorious Service Medal. While in the military she worked from Avionics Electronics technician to Wing/Base level Training Manager, Helicopter Rescue. Ellen has to her credit degrees in Human Relations (MA), Anthropology (BA), and Instructor in Technology (AS) degrees earned while on the job. Peer Review: This article has been internationally peer-reviewed and accepted for publication according to the guidelines given at the journal’s website. Note: This article was accepted and presented at IAJC-ASEE International Joint Conference on Engineering and Related Technologies sponsored by IAJC, ASEE, and IEEE, venue at University of Hartford, USA during April 29-30, 2011. *Corresponding author (Tom Munns). E-mail addresses: tgmunns@gmail.com, fink@tamu.edu, onderko@tamu.edu. 2011. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 2 No.5 (Special Issue). 545 ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/531-545.pdf