Mixtrinsic Multi-Objective Reconfiguration of Evolvable Sensor Electronics

Recently, evolvable hardware has been developed with the objective to deal with sensor electronics problems such as static and dynamic deviations. However the industrial specifications and requirements are not considered in the hardware-learning loop during the intrinsic optimization. Indeed, it minimizes the error between the required output and the real output generated by a given test signal. In our work, we optimize the standard specifications of the hardware to obtain predictable behavior hardware. However, some of the industrial specifications need expensive equipments and some others are time consuming. In this paper we introduce a new approach, which simulates a set of the specifications that is hard to be measured due to the cost/time requirements, e.g., A
₀
, Phi, etc. On the other hand, the set of specifications that is more sensitive to the instance deviations is measured intrinsically, e.g., offset, CMR, etc. We employ the programmable operational amplifier from L. Lakshmanan et al., (2005) as a case study. Our approach succeeds to optimize the amplifier to meet the industrial specifications at low cost setup.