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- W4386242042 abstract "Fully programmable valve array (FPVA) biochips have emerged as a promising alternative for traditional application-specific microfluidic platforms thanks to their advantages in terms of flexibility and reconfigurability. By regularly deploying microvalves along vertical and horizontal flow channels, microfluidic modules with different sizes and shapes can be constructed dynamically on the chip, thereby enabling the automatic execution of various assay procedures in biology and biochemistry. The above advantages, however, result largely from the large-scale integration of valves as well as accurate control of their switchings, leading to very complicated control-logic design of such chips. In this paper, we propose a reinforcement learning-based synthesis flow for the control-logic design of FPVA biochips, taking multichannel switching and control-cost minimization into consideration simultaneously. By employing a double deep Q-network and two Boolean-logic simplification techniques, control logics with both high switching efficiency and low fabrication cost can be constructed automatically. Furthermore, the solution space of multichannel-switching combinations is reduced to improve the search efficiency of the proposed method. Experimental results on multiple benchmarks demonstrate that the proposed synthesis flow leads to better design solutions compared with the state-of-the-art techniques." @default.
- W4386242042 created "2023-08-30" @default.
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- W4386242042 date "2023-01-01" @default.
- W4386242042 modified "2023-10-16" @default.
- W4386242042 title "Control-Logic Synthesis of Fully Programmable Valve Array Using Reinforcement Learning" @default.
- W4386242042 doi "https://doi.org/10.1109/tcad.2023.3309740" @default.
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