Low temperature Cu-Cu thermocompression bonding assisted by electrochemical desorption of a self-assembled monolayer
This paper reports low temperature Cu-Cu thermocompression bonding with the help of self-assembled monolayer (SAM) desorption. SAM layer protects copper from oxidation. It should be desorbed just before bonding. The desorption was carried out using cyclic voltammetry in aqueous potassium hydroxide (KOH) solution. Contact angle measurements carried out before and after desorption indicates successful desorption of SAM. The bonding was carried out at 150 °C and has yielded in excellent bond strength of 520 N.
Temporary passivation of Cu for low temperature (&#60; 300°C) 3D wafer stacking
Self-assembled monolayer (SAM) of alkane-thiol is applied to provide temporary passivation on Cu surface for protection against oxidation. The thermal stability of SAM is improved by storage at low temperature and in inert ambient. Surface analysis shows that SAM is effective in retarding surface oxidation and the degree of protection is higher for SAM with longer chain length. Wafer-to-wafer bonding result shows shear strength enhancement in the Cu-Cu bond when the appropriate chain length is used. Longer chain is less susceptible to thermal desorption hence no improvement in the bond strength is obtained.
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.