Capacitive DACs (C-DACs) are widely used as stand-alone DACs or in an ADC as auxiliary DACs. An important performance metric of a C-DAC is its energy consumption and the linearity between the digital input and the analog output. In multi-bit C-DACs, the mismatch between the capacitors can degrade linearity, which can be important in high-resolution applications. In this work, we analyze the power consumption and linearity performance of a class of C-DACs called split-array C-DACs. We show that the simple element rotation technique, which is widely used to suppress the mismatch error of DACs, cannot be used with the power-efficient three-level switching scheme to effectively suppress the mismatch error. Then, we propose a switching scheme which can be used with the power efficient three-level switching and can suppress the in-band mismatch error effectively.

Selective laser melting (SLM) is a combined process of melting and stacking three-dimensional products by fusing micro-metal powder using a laser. It has the advantage of manufacturing parts with complex structures with reduced production time. However, in the case of aluminum, the disadvantages of poor laser formability due to its high thermal conductivity, diffusivity, and reflectivity result in process defects such as bowling, pore, and poor surface quality. This study aims to develop a surface defect removal methodology during aluminum melting by laser processing and to enhance process automation capabilities by introducing a sensor monitoring scheme. In the laser experiments, aluminum specimens (AL-7075) with mechanical scratches were used and the level of surface defect removal during processing was classified depending on surface conditions. In addition, a PVDF-type acoustic emission (AE) sensor monitoring system was implemented to collect characteristic signals during the laser polishing of the machined surface(scratch). It was shown that the degree of surface defects removal and surface state could be effectively classified through a convolution neural network (CNN) utilizing the collected signals as input vectors.