Stress–Strain Behavior of SAC305 at High Strain Rates

Abstract

Electronic products are subjected to high G-levels during mechanical shock and vibration. Failure-modes include solder-joint failures, pad cratering, chip-cracking, copper trace fracture, and underfill fillet failures. The second-level interconnects may be experience high strain rates and accrue damage during repetitive exposure to mechanical shock. Industry migration to lead-free solders has resulted in proliferation of a wide variety of solder alloy compositions. One of the popular tin-silver-copper alloys is Sn3Ag0.5Cu. The high strain rate properties of lead-free solder alloys are scarce. Typical material tests systems are not well suited for measurement of high strain rates typical of mechanical shock. Previously, high strain rates techniques such as the split Hopkinson pressure bar (SHPB) can be used for strain rates of 1000 s−1. However, measurement of materials at strain rates of 1–100 s−1 which are typical of mechanical shock is difficult to address. In this paper, a new test-technique developed by the authors has been presented for measurement of material constitutive behavior. The instrument enables attaining strain rates in the neighborhood of 1–100 s−1. High-speed cameras operating at 300,000 fps have been used in conjunction with digital image correlation (DIC) for the measurement of full-field strain during the test. Constancy of crosshead velocity has been demonstrated during the test from the unloaded state to the specimen failure. Solder alloy constitutive behavior has been measured for SAC305 solder. Constitutive model has been fit to the material data. Samples have been tested at various time under thermal aging at 25 °C and 125 °C. The constitutive model has been embedded into an explicit finite element framework for the purpose of life-prediction of lead-free interconnects. Test assemblies has been fabricated and tested under Joint Electron Device Engineering Council (JEDEC) JESD22-B111 specified condition for mechanical shock. Model predictions have been correlated with experimental data.