AUTOMATIC TEST EQUIPMENT AND BENCH TEST CORRELATION by Alan Aragon, B.S.E.E. A THESIS IN ELECTRICAL ENGINEERING Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in ELECTRICAL ENGINEERING Dr. Richard Gale Committee Chairman Dr. Stephen Bayne Dr. Ron Cox Dr. Tim Dallas Dr. Dominick Joseph Casadonte Jr. Dean of the Graduate School August, 2013 c 2013 Alan Aragon All Rights Reserved Texas Tech University, Alan Aragon, August 2013 TABLE OF CONTENTS ABSTRACT . iii LIST OF TABLES . iv LIST OF FIGURES . v I INTRODUCTION . 1 IIPART..................................... 2 III HARDWARE . 5 3.1 Bench Hardware Analysis Setup . 5 3.2 Bench Hardware Analysis . 5 3.3 ATE Hardware Analysis Setup . 10 3.4 ATE Hardware Analysis . 10 IV SOFTWARE . 15 4.1 Design Environment Considerations . 15 4.2 Software Considerations . 17 4.3 Software Implementation . 18 V STATISTICS AND MATH . 21 VI TEST IMPLEMENTATION . 27 VII CORRELATION DATA . 33 VIII CONCLUSION . 43 BIBLIOGRAPHY . 44 APPENDICES A ICHG AND VBREG DATA FULL TABLES . 46 B BASIC ANALYSIS OF ATE MEASUREMENT METHOD . 53 C BASIC ANALYSIS OF BENCH MEASUREMENT METHOD . 56 ii Texas Tech University, Alan Aragon, August 2013 ABSTRACT The purpose of this research is to create an automated method to collect bench data for Automatic Test Equipment (ATE) tested parameters in order to identify correlation offsets if any on critical device parameters and to identify root causes of these correlation offsets. The devices under test are all single cell battery chargers that have stringent spec requirements over operating temperature range. In production tests these parameters are tested for on the ATE and parts not meeting spec are screened out. Hidden correlation offsets between ATE results and bench test results could mean that parts that actually lie outside specifications could be shipped to end customers. iii Texas Tech University, Alan Aragon, August 2013 LIST OF TABLES 3.1 List of hardware................................6 4.1 Comparison between LabVIEW graphical, LabVIEW CVI, and C# design environments.................................. 15 5.1 Standard deviation population percentages.................. 22 7.1 ILIM data from the ATE........................... 33 7.2 ILIM data from the bench........................... 34 7.3 ILIM data percentage difference between ATE and bench.......... 34 7.4 ICHG data from the ATE........................... 35 7.5 ICHG data from the bench test........................ 35 7.6 ICHG data percentage difference....................... 36 7.7 ICHG data percentage difference after correlation factor is applied..... 37 7.8 VBREG data from the ATE.......................... 40 7.9 VBREG data from the bench test....................... 41 7.10 VBREG data percentage difference...................... 42 A.1 ICHG data from the ATE........................... 46 A.2 ICHG data from the bench test........................ 47 A.3 ICHG data percentage difference....................... 48 A.4 ICHG data percentage difference after correlation factor is applied..... 49 A.5 VBREG data from the ATE.......................... 50 A.6 VBREG data from the bench test....................... 51 A.7 VBREG data percentage difference...................... 52 iv Texas Tech University, Alan Aragon, August 2013 LIST OF FIGURES 2.1 BQ2425x datasheet pg. 1. [1].........................3 2.2 BQ2425x block diagram. [1].........................4 3.1 Hardware block diagram............................5 3.2 Voltage measurement setup. VIN = 7.5V....................6 3.3 Current measurement setup. VIN = 1.5V. RAGI=1 kW±1% RKEI=12.5 kW± 1%.......................................7 3.4 Agilent 34401a voltage measurement zoomed in. Sample number is of each sample taken. s = 1.75048×10−5, m = 4.97165.............8 3.5 Agilent 34401a voltage measurement with KA7805 specifications......9 3.6 Agilent 34401a current measurements zoomed in. s = 1.44356×10−7, m = 1.49095×10−3................................ 10 3.7 Agilent 34401a current measurements with possible current values of the resistor..................................... 11 3.8 Keithley 2440 voltage measurement zoomed in. s = 1.79594×10−5, m = 4.97163..................................... 12 3.9 Keithley 2440 voltage measurement with KA7805 specifications....... 12 3.10 Keithley 2440 current measurement zoomed in. s = 1.0908×10−8, m = 1.19352×10−4................................. 13 3.11 Keithley 2440 current measurement with KA7805 specifications....... 13 3.12 ATE voltage measurements.......................... 14 3.13 ATE current measurements........................... 14 4.1 Input Current Limit test tab.......................... 19 4.2 Charge Current test tab............................. 20 4.3 Battery Voltage Regulation test tab....................... 20 5.1 A normal distribution.............................. 21 5.2 Visual example of the standard deviation population percentages....... 22 5.3 Cp and Cpk visualization.[2]......................... 25 6.1 BQ2425x typical charging profile. [1].................... 30 6.2 Example battery. [3]............................. 31 7.1 ICHG correlation graph............................ 36 7.2 VBREG correlation graph........................... 39 B.1 Forcing voltage, measuring current. [4]................... 53 B.2 Forcing current, measuring voltage. [4]................... 55 C.1 Dual-slope circuit. [5]............................. 56 C.2 Multi-slope run-up circuit. [5]........................ 57 v Texas Tech University, Alan Aragon, August 2013 CHAPTER 1 INTRODUCTION The motivation behind this thesis is to create an additional method of testing that would identify correlation offsets in a simple and effective manner. The ATE is the primary ma- chine to test parameters for units in development. While these machines are excellent for testing, an additional method of testing is needed to identify if there are any correlation offsets. This additional method will be bench testing. It allows for the testing of a small number of parts without the complexity of the ATE. The bench tests can also be automated to allow a sample size that is large enough to see a correlation offset if any exists. To auto- mate the bench tests, software would be created to control the bench equipment and gather the data. It would also allow any engineer to quickly setup the bench test because it would be created to be simplistic. The automated tests would also allow start-and-forget testing of the units to permit the engineer to perform other tasks while the data is gathered for later analysis. The correlation offsets are usually hidden and are unknown until the correlation is completed. With the automated bench test these hidden offsets, if any, will be found quickly. 1 Texas Tech University, Alan Aragon, August 2013 CHAPTER 2 PART The part that will be used in the development of the bench test software will be the BQ24250 developed by Texas Instruments. The BQ24250 (250) is a single cell Li-Ion battery charger. The 250 can be programmed to change a variation of parameters such as; input current limit, charge voltage, and charge current. It also has built in self protection. The figures above show the first page of the BQ2425x datasheet (see Fig. 2.1) and the block diagram of the BQ2425x (see Fig. 2.2). The 250 typically charges batteries that are in the 3.5 to 4.4 V range. Some applications that the 250 can be used in are cellular devices, cameras, MP3 players and portable equipment. Three critical parameters of the 250 are input current, charge current, and charge voltage. Since the input to the 250 varies, it is essential that the input current be correct. The input current also must not exceed the current of the source. Charge current management for the 250 is also crucial. It determines the duration it takes for the battery cell to be charged. Too slow and the patience of the user is exceeded. Too fast and the battery can be damaged. The charge current should be the maximum it can be without damaging the battery. The charge voltage parameter is important because it determines if the battery is charged properly. It prevents overcharging and undercharging of the battery. If the battery is overcharged, then it can be damaged. If the battery is undercharged, then it will not have the expected life expectancy and the product using the battery will run out of charge quicker than expected. 2 Texas Tech University, Alan Aragon, August 2013 Figure 2.1: BQ2425x datasheet pg. 1. [1] 3 Texas Tech University, Alan Aragon, August 2013 Figure 2.2: BQ2425x block diagram. [1] 4 Texas Tech University, Alan Aragon, August 2013 CHAPTER 3 HARDWARE 3.1 Bench Hardware Analysis Setup The software is connected to a simple hardware setup. To perform the tests that are being considered there are several pieces of bench equipment needed. The equipment used with this software is a power supply, a source meter, and three digital multi-meters. To talk to the EVM, a USB interface adaptor developed by Texas Instruments is used. To talk to the bench equipment, a USB to GPIB connected developed by National Instruments is used. These adaptors are connected to a computer where the software will be run. A simple block diagram of how the hardware is connected is shown in Figure 3.1. Table 3.1 shows which instruments were specifically used in this thesis. Figure 3.1: Hardware block diagram. 3.2 Bench Hardware Analysis It is important that the hardware being used to perform these test be accurate and pre- cise. If