The increasing demand in automotive markets is leading the semiconductor industries to develop high-performance and highly reliable power devices. Silicon carbide MOSFET chips are replacing silicon-based solutions through their improved electric and thermal capabilities. In order to support the development of these novel semiconductors, packaging technologies are evolving to provide enough reliable products. Silver sintering is one of the most promising technologies for die attach. Due to their superior reliability properties with respect to conventional soft solder compounds, dedicated reliability flow and physical analyses should be designed and employed for sintering process optimization and durability assessment. This paper proposes an experimental methodology to optimize the pressure value applied during the silver sintering manufacturing of a silicon carbide power MOSFET molded package. The evaluation of the best pressure value is based on scanning electron microscopy performed after a liquid-to-liquid thermal shock reliability test. Furthermore, the sintering layer degradation is monitored during durability stress by scanning the acoustic microscopy and electric measurement of a temperature sensitive electric parameter. Moreover, mechanical elastoplastic behavior is characterized by uniaxial tensile test for a bulk sample and finite element analysis is developed to predict the mechanical behavior as a function of void fraction inside sintering layer.

Semiconductor power modules are the key hardware components of a traction inverter. It drives motor speed and torque, managing the energy exchange from battery to motor and viceversa. The increasing demand for electric and hybrid vehicle requests high performance power modules. Power semiconductor devices based on wide band gap compound, like silicon carbide (SiC), have excellent electrical properties in terms of on-state resistance, stray inductance and performance at high commutation frequency. One of the most promising solution is silicon carbide MOSFET power module in which each switch is made by several different dies placed in parallel. Embedded direct cooling system and novel materials with high conductivity (e.g., active metal brazed substrates) can be considered to enhance thermal performance. A robust method is needed to characterize and to predict power module temperature behavior considering the importance of the thermal performance to improve reliability and to optimize module weight and dimensions. According to several parallel dies inside each switch, classic method based on temperature electric sensitive parameter (TSEP) shall be validated with direct measurement. In this framework, it has been reported the thermal characterization of a power module for a traction inverter based on eight silicon carbide MOSFETs for each switch. Both TSEP and infrared measurements have been employed. Thermal behavior has been numerically reproduced, creating a simplified equivalent network and developing a predictive model by finite element method (FEM).

Solder reliability is a key aspect for the packaging of low voltage power semiconductor device. The interconnections among package components, e.g. the silicon chip and copper leadframe, and between package itself and the external printed control board (PCB) should be properly designed to ensure the automotive durability requirements. In this framework, the proposed paper introduces an experimental-numeric characterization flow with the purpose to analyze solder visco-plasticity and fatigue during passive temperature cycle. The presented methodology has included solder mechanical characterization aimed to determine the parameters of Anand model which reproduces the solder visco-plastic behavior and the mechanical properties’ temperature dependency. Finite element model has been employed to calculate the inelastic work which solder dissipates during each temperature cycle. Simulation results serve as input to predict solder lifetime according to an energetic method. Moreover, failure analyses have been performed to assess the failure mechanism and to check model correlation in terms of number of cycles to failure forecast.