PA-level Current Detection Theory And Instrument Design

Microcurrent detection technology has important applications in many fields,and promotes the development of weak signal detection.In this paper,through the research on the micro-current detection theory and the digital noise reduction method,a practical circuit is designed and the noise reduction method is embedded in the core processor to realize the detection of pA level current.By analyzing the advantages and disadvantages of sampling resistance type and negative feedback type current amplifier circuit to realize micro current detection,it is known that the negative feedback current amplifier circuit is more suitable for micro current detection.And the simulation analysis proves that the circuit noise introduced by the large resistance is smaller;through the analysis of the frequency characteristics of the circuit,it can be seen that the tiny stray capacitance and input capacitance will change the frequency characteristics of the circuit,and the time constant R1C1 is equal to the time constant RFCF.To suppress stray capacitance,use shielding measures and the shortest possible input cable to suppress input capacitance.For the problem of offset voltage,comparing the two methods of eliminating offset voltage with resistor and voltage source,although both can have a good suppression effect on the offset voltage,the dynamic performance of the voltage source is better and less noise is introduced.Aiming at the interference of the power supply ripple on the output signal of the op amp,it is known that the fixed power supply ripple affects the effective bits of the analog-to-digital converter and the maximum power supply ripple that has the least impact on the accuracy of the analog-to-digital converter.It can effectively reduce the interference to the output signal of the operational amplifier.The adaptive filtering method has been widely used in many fields of weak signal detection due to its low computational complexity,good stability and good convergence.In order to improve the signal-to-noise ratio of weak signal detection,a linear constrained adaptive filtering method based on multi-objective genetic algorithm optimization is proposed.In this method,linear constraints are added to prevent the weight of the minimum mean square error algorithm from shifting.A penalty function is used to convert the constraint problem to an unconstrained problem,and a multi-objective genetic algorithm is used to optimize the weights.Through the simulation experiment method,the algorithm is compared with the linear constraint adaptive noise cancellation method and the linear constraint adaptive noise cancellation method based on genetic algorithm optimization in terms of mean square error,steady-state error and smoothness,which proves the algorithm proposed in this thesis.With good noise reduction performance,useful signals can still be extracted when the amplitude signal-to-noise ratio reaches-60 d B.At the same time,the algorithm also improves the contradiction between signal smoothness and convergence speed and steady-state error.Finally,in order to solve the problem that the amplitude of the output signal of the instrument shows a linear growth trend within the 1000Hz～2000Hz bandwidth,the algebraic equation algorithm of three-parameter least square fitting is used to correct the data.The average growth rate of the modified data is reduced by about 52%,and the average error is 1.368%.