可重构传感器电子的粒子群优化-案例研究:3位闪存ADC

Peter Tawdross, A. König
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引用次数: 17

摘要

传感器电子在嵌入式系统中无处不在,但其性能容易受到静态和动态偏差的影响。即使昂贵和耗时的激光切边仍然不能处理所有出现的偏差。最近,模拟可重构电子提供了一种解决方案来补偿这些影响。目前的技术使用遗传算法(GA)来找到任意拓扑以满足给定的规范,这可能导致硬件具有不可预测的行为。考虑到重构方法的鲁棒性,我们使用Tawdross, P.和Konig, A.(2005)的粒子群优化(PSO)作为遗传算法的替代方案,用于Tawdross, P.等人(2005)在基本块级别上对可编程传感器电子设备进行重构。为了发展具有可预测性能的可靠硬件,采用了标准电路拓扑。本文将设计环境从放大器级抽象到功能模块级。我们通过可重构的3位闪存模拟数字转换(ADC)演示了我们的方法,该方法可以成功地从静态和动态偏差中恢复
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Particle Swarm Optimization for Reconfigurable Sensor Electronics - Case Study: 3 Bit Flash ADC
Sensor electronics is ubiquitous in embedded systems, yet its performance is susceptible to static and dynamic deviations. Even costly and time consuming laser trimming still can't deal with all the occurring deviations. Recently, analog reconfigurable electronics offers a solution to compensate these effects. The state of the art uses genetic algorithm (GA) to find an arbitrary topology to fulfil the given specifications, which can cause hardware with unpredictable behavior. Considering the robustness of the reconfiguration approach, we used the particle swarm optimization (PSO) by Tawdross, P. and Konig, A. (2005) as an alternative to GA for reconfiguration of programmable sensor electronics by Tawdross, P. et al. (2005) on basic block level. In order to evolve a reliable hardware with predictable performance, standard circuit topologies are employed. In this paper, we abstract our design environment from amplifier level to the functional block level. We demonstrate our methodology by a reconfigurable 3-bit flash analog to digital convert (ADC), which can recover successfully from static and dynamic deviations
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