Mismatch Reduction Techniques for Current-Mirror Based Potentiostats

Haberler, Markus; Siegl, Inge; Steffan, Christoph; Auer, Mario

doi:10.23919/mipro48935.2020.9245441

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…The basic problem of fabricating circuits in nanometer technologies is the mismatch of process parameters leading to a change in the thickness of the gate oxide in field-effect transistors [ 38 ]. As for SNNs, the change in threshold voltages of MOS transistors caused by this phenomenon leads to a change in scaling factors of the current mirror multipliers, and thus in the weights of connections in the network [ 39 ]. The mismatch problem is the basic source of damages to integrated circuits at the fabrication stage and requires proposing effective testing methods [ 40 ].…”

Section: Pattern Classificationmentioning

confidence: 99%

Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry

Szczęsny

Huderek

Przyborowski

2021

Sensors

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The paper describes the architecture of a Spiking Neural Network (SNN) for time waveform analyses using edge computing. The network model was based on the principles of preprocessing signals in the diencephalon and using tonic spiking and inhibition-induced spiking models typical for the thalamus area. The research focused on a significant reduction of the complexity of the SNN algorithm by eliminating most synaptic connections and ensuring zero dispersion of weight values concerning connections between neuron layers. The paper describes a network mapping and learning algorithm, in which the number of variables in the learning process is linearly dependent on the size of the patterns. The works included testing the stability of the accuracy parameter for various network sizes. The described approach used the ability of spiking neurons to process currents of less than 100 pA, typical of amperometric techniques. An example of a practical application is an analysis of vesicle fusion signals using an amperometric system based on Carbon NanoTube (CNT) sensors. The paper concludes with a discussion of the costs of implementing the network as a semiconductor structure.

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Section: Pattern Classificationmentioning

confidence: 99%

Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry

Szczęsny

Huderek

Przyborowski

2021

Sensors

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“…It is the simplest and most effective method to obtain real-time motor winding current and achieve lossless current detection. Compared with other methods, sense FET current sensing can simplify the circuit design, but due to the large difference in aspect ratio between the power transistor and sensing transistor, there will be a current mismatch [14,15]. The amount of mismatch is directly reflected in the error between the detected current and actual current, which has a great impact on the accuracy.…”

Section: Introductionmentioning

confidence: 99%

An On-chip Integrated Current Sensing Technology for Real-time Detection of Phase Current in BLDC Motors

Wen,

Wang

2023

PIER M

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This paper presents an on-chip integrated sensing circuit for real-time detection of phase currents in three-phase brushless direct current (BLDC) motors. The three-phase sinusoidal currents generated in the motor winding are detected by an innovative Sense FET technology, which can accurately measure the currents of high side and low side power transistors simultaneously. A dynamic matching elimination method is proposed for the detection current mismatch problem due to the large difference in aspect ratio. Using a 90 nm BCD process for design and verification, the detection circuit of the high side and low side can well follow the change of sine wave phase current of the three-phase motor. The detection accuracy is above 96%, and the best accuracy can reach 99.219%. The elimination effect of circuit current mismatch is obvious, and the error of the sensing current can be reduced by 38.9%.

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“…The accuracy and sensitivity of the current mirror are essential in characterizing and processing the biosensor current signal. It is highly influenced by the matching characteristics of the transistors used in the current mirror [4,5]. A current mirror with mismatched transistors (e.g.…”

Section: Introductionmentioning

confidence: 99%

Precise method for accuracy and sensitivity improvement of a current mirror for very low current measurement on a dissolved oxygen biosensor

Umar

Setiadi²,

Linda³

et al. 2022

Meas. Sci. Technol.

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A dissolved oxygen measurement using an electrochemical biosensor and conventional current mirror with an accurate result and desired sensitivity is diﬃcult to achieve, though this current mirror is used frequently to process the biosensor signal and providing good response. However, it exhibits some drawbacks particularly due to mismatched-transistors, which will lead to asymmetry between input and output currents. This asymmetry causes the unwanted oﬀset and gain error, making reduction of its accuracy, especially on very low current. A modiﬁed current mirror utilizing precise gate voltage adjustment of FET transistors is applied to match the transistors’ currents. The results show accuracy improvement of the modiﬁed current mirror compared to the conventional current mirror, where the improvements provide a very low accuracy error of 0.01%. In addition, the current mirror’s sensitivity can be adjusted by implementing this modiﬁcation without increasing much noise.

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Mismatch Reduction Techniques for Current-Mirror Based Potentiostats

Cited by 3 publications

References 12 publications

Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry

Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry

An On-chip Integrated Current Sensing Technology for Real-time Detection of Phase Current in BLDC Motors

Precise method for accuracy and sensitivity improvement of a current mirror for very low current measurement on a dissolved oxygen biosensor

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