Page 36


www.us-tech.com


March, 2018


Enhanced Manufacturing Services 4.0 Part 3: Finding Gold in Production and Test Data


By Christophe Lotz, President and CEO, ASTER Technologies, LLC W


hen looking at quality data from products returned by end users, there is a key met-


ric: no fault found (NFF). According to a report published by consulting


There are different interpreta-


tions of NFF according to various in- dustries. Most often, NFF means a passed product that has failed at the customer’s site. Sometimes it is a


tection by using test functions. The defects that are detected


during board-level integration or sys- tem-level test should have only two sources:


l The defect occurs later, after board


test (damage or aging) and/or the fault is intermittent.


l The defect is already on the board


prior to the integration level, but the board test is not able to detect the defect. This might be due to a lack of coverage (slip), wrong cover- age metrics or a defect universe, something entirely misaligned from the production environment.


Traceability and Repair Tool In a previous article, test cover-


QUAD system overview.


firm Accenture, around 70 percent of all product returns in the U.S. were characterized as NFF. Cost-wise, in- cluding returns processing, scrap and liquidation, NFF amounted to 50 percent of a $13.8 billion return and repair cost. Through the BASTION research program, ASTER and a con- sortium of European universities and industrial partners have investigat- ed NFF.


failed product at the customer’s site, which is returned and then passes at the production facility. The NFF phenomenon is ex-


tremely difficult to study, given the need to analyze faults that have not been detected. As they are not detect- ed, there is no data to draw from to understand root causes or actions to take. It is challenging to anticipate the defect occurrence and defect de-


age was introduced as the key quali- fication tool for driving schematic de- sign, board layout and test program development, as well as enabling the best production yield. A number of different test cov-


erage models, ranging from MPS to PCOLA/SOQ and PPVS, have been developed. These define how the cov- erage metric is calculated using dif- ferent defect models. Since test coverage and defects


per million opportunities (DPMO) are strongly linked through the pro- duction model, knowledge of proba- ble defects is as important as the knowledge of test coverage. ASTER is developing its QUAD


(QUalityADvisor), a flexible and modular software tool, built around a centralized and open-architecture database. The tool provides trace- ability for PCB production data. It helps to accurately retrieve data and convert it into meaningful informa- tion that can be used to fine-tune the product lifecycle.


Big Data Products are passed through a


test line, which, step-by-step, detects specific types of defects. The informa- tion is stored in a centralized data- base for traceability. With Industry 4.0, raw data from various machines are aggregated to build and visualize comprehensive information that al- lows the manufacturer to understand the defect universe. Both defects per million (DPM)


and DPMO are used to determine the overall quality of the unit under test (UUT), produced from the sample quantity inspected. DPM is a meas- ure of manufacturing throughput — how many bad parts slip through. DPMO is a measure of performance — how many times a manufacturing defect occurs. DPMO is also an indi- cator of which manufacturing processes need improvement. Test strategy and defect occur-


rences should be linked together so improved test coverage can be aimed


Continued on next page


See at APEX, Booth 2909


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80  |  Page 81  |  Page 82  |  Page 83  |  Page 84  |  Page 85  |  Page 86  |  Page 87  |  Page 88  |  Page 89  |  Page 90  |  Page 91  |  Page 92  |  Page 93  |  Page 94  |  Page 95  |  Page 96  |  Page 97  |  Page 98  |  Page 99  |  Page 100  |  Page 101  |  Page 102  |  Page 103  |  Page 104  |  Page 105  |  Page 106  |  Page 107  |  Page 108  |  Page 109  |  Page 110  |  Page 111  |  Page 112  |  Page 113  |  Page 114  |  Page 115  |  Page 116  |  Page 117  |  Page 118  |  Page 119  |  Page 120  |  Page 121  |  Page 122  |  Page 123  |  Page 124  |  Page 125  |  Page 126  |  Page 127  |  Page 128  |  Page 129  |  Page 130  |  Page 131  |  Page 132  |  Page 133  |  Page 134  |  Page 135  |  Page 136