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After decades of development, flow-based microfluidic biochips have become an increasingly attractive platform for biochemical experiments. The fluid transportation and the on-chip device operation are controlled by microvalves, which are driven by external pneumatic controllers. To meet the increasingly complex experimental demands, the number of microvalves has significantly increased, making it necessary to adopt multiplexers (MUXes) for the actuation of microvalves. However, existing MUX designs have limited coding capacities, resulting in area overhead and excessive chip-to-world interface. This paper proposes a novel gate structure for modifying the current MUX architecture, along with a mixed coding strategy that achieves the maximum coding capacity within the modified MUX architecture. Additionally, an efficient synthesis tool for the mixed-coding-based MUXes (LaMUXes) is presented. Experimental results demonstrate that the LaMUX is exceptionally efficient, substantially reducing the usage of pneumatic controllers and microvalves compared to existing MUX designs. 

Cyclotron Radiation Emission Spectroscopy (CRES) is a technique for precision measurement of the energies of charged particles, which is being developed by the Project 8 Collaboration to measure the neutrino mass using tritium beta-decay spectroscopy. Project 8 seeks to use the CRES technique to measure the neutrino mass with a sensitivity of 40 meV, requiring a large supply of tritium atoms stored in a multi-cubic meter detector volume. Antenna arrays are one potential technology compatible with an experiment of this scale, but the capability of an antenna-based CRES experiment to measure the neutrino mass depends on the efficiency of the signal detection algorithms. In this paper, we develop efficiency models for three signal detection algorithms and compare them using simulations from a prototype antenna-based CRES experiment as a case-study. The algorithms include a power threshold, a matched filter template bank, and a neural network based machine learning approach, which are analyzed in terms of their average detection efficiency and relative computational cost. It is found that significant improvements in detection efficiency and, therefore, neutrino mass sensitivity are achievable, with only a moderate increase in computation cost, by utilizing either the matched filter or machine learning approach in place of a power threshold, which is the baseline signal detection algorithm used in previous CRES experiments by Project 8.