In this study, we adopt a model-based correction method to reduce the finite aperture effect in photoacoustic tomography (PAT)--the tangential resolution deteriorates as the imaging point moves away from the circular scanning center. Such degradation in resolution originates from the spatial impulse responses (SIRs) of the used finite-sized unfocused transducer. Based on a linear, discrete PAT imaging model, the proposed method employs a spatiotemporal optimal filter designed in minimum mean square error sense to compensate the SIRs associated with an unfocused transducer at every imaging point; thus retrospective restoration of the tangential resolution can be achieved. Simulation and experimental results demonstrate that this method can substantially improve the degraded tangential resolution for PAT with finite-sized unfocused transducers while retaining the radial resolution.

Several design parameters affect the reliability of wafer-level type advanced packaging, such as upper and lower pad sizes, solder volume, buffer layer thickness, and chip thickness, etc. Conventionally, the accelerated thermal cycling test (ATCT) is used to evaluate the reliability life of electronic packaging; however, optimizing the design parameters through ATCT is time-consuming and expensive, reducing the number of experiments becomes a critical issue. In recent years, many researchers have adopted the finite-element-based design-on-simulation (DoS) technology for the reliability assessment of electronic packaging. DoS technology can effectively shorten the design cycle, reduce costs, and effectively optimize the packaging structure. However, the simulation analysis results are highly dependent on the individual researcher and are usually inconsistent between them. Artificial intelligence (AI) can help researchers avoid the shortcomings of the human factor. This study demonstrates AI-assisted DoS technology by combining artificial intelligence and simulation technologies to predict wafer level package (WLP) reliability. In order to ensure reliability prediction accuracy, the simulation procedure was validated by several experiments prior to creating a large AI training database. This research studies several machine learning models, including artificial neural network (ANN), recurrent neural network (RNN), support vector regression (SVR), kernel ridge regression (KRR), K-nearest neighbor (KNN), and random forest (RF). These models are evaluated in this study based on prediction accuracy and CPU time consumption.

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