CMOS Readout Circuit Developments for Ion Sensitive Field Effect Transistor Based Sensor Applications

Chung, Wen-Yaw; Reidj, Febus; Cruz, Guillermo J.; Yang, Chen; He, Fu-Shun; Liu, Tai-Tsun; Pijanowska, D. G.; Torbicz, W.; Grabiec, P.

doi:10.5772/6891

Cited by 14 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, VDS and IDS should remain constant. Satisfying these conditions allowed for the measurement of ion activities (pH and Ca 2+ concentration) through the VGS voltage variation [12]. The last layer, the single-board computer, communicated with the microcontroller to collect the measured data and send the user commands, as well as configuration and setting parameters.…”

Section: Potentiometric Readout Circuitmentioning

confidence: 99%

See 1 more Smart Citation

Development of a Portable Multi-Sensor Urine Test and Data Collection Platform for Risk Assessment of Kidney Stone Formation

et al. 2020

Self Cite

View full text Add to dashboard Cite

In this paper, we present an Internet of things (IoT)-based data collection system for the risk assessment of urinary stone formation, or urolithiasis, by the measurement and storage of four parameters in urine: pH, concentrations of ionized calcium (Ca2+), uric acid and total dissolved solids. The measurements collected by the system from patients and healthy individuals grouped by age and gender will be stored in a cloud database. These will be used in the training phase of an artificial intelligence (AI) machine learning process utilizing the logistics regression model. The trained model provides a binary risk assessment, indicating if the end user is either a stone-former or not. For system validation, standard chemical solutions were used. Preliminary results indicated a sufficient measurement range, falling within the physiological range, and resolution for pH (2.0–10.0, +/−0.1), Ca2+(0.1–3.0 mmol/l, +/−0.05), uric acid (20–500 ppm, +/−1) and conductivity (1.0–40.0 mS/cm, +/−0.1), exhibiting high correlation with standard instruments. We intend to deploy this system in few hospitals in Taiwan to collect the data of patients’ urine, with analysis aided by urologist assessments for the risk of urolithiasis. The modularized design allows future modification and expansion to accommodate other sensing analytes.

show abstract

Section: Potentiometric Readout Circuitmentioning

confidence: 99%

Section: Potentiometric Readout Circuitmentioning

confidence: 99%

Development of a Portable Multi-Sensor Urine Test and Data Collection Platform for Risk Assessment of Kidney Stone Formation

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Тому були використані p-канальні польові транзистори, оскільки внаслідок на-явності в окислі певного позитивного заряду, що виникає в процесі термічного окиснення кремнію і розташований поблизу межі розділу з кремнієм [18], саме в таких транзисторах не може утворюватися інверсійний вбудований канал провідності під діелектриком як актив-ної, так і пасивної областей, а тому немає не-обхідності у додаткових технологічних опера-ціях ізоляції кожного транзистору на кристалі [3,11]. Крім того, при прикладанні від'ємної робочої напруги до затвору через розчин елек-троліту виникає анодна поляризація діелек-трика, при якій його поверхня лише додатково окислюється, і таким чином не відбувається її деградація, як у випадку n-канальних ІСПТ [3,19]. В той же час для збільшення крутості перехідної характеристики затворну область було виконано зигзагоподібної форми з відно-шенням W/L=100 при довжині каналу 40 мкм, що забезпечувало достатньо високий коефіці-єнт підсилення.…”

Section: оптимізація параметрів іспт-елемен-тівunclassified

Calculation of Electro-Physical Parameters of Ion-Selective Field Effect Transistors: Theoretical and Practical Aspects

Лозовий¹,

Кукла²,

Павлюченко³

et al. 2013

SEMST

View full text Add to dashboard Cite

“…This implementation focus on optimum biasing current for ISFET and time-variant compensation algorithm. In [5] propose an improvement of bridge-type floating source circuit read-out circuit technique. In [6] studied the diffusion sensor interface and sensor insulation development that is more specific to the sensor element improvement.…”

Section: Introductionmentioning

confidence: 99%

Drift Compensation for pH IsFET Sensor Using NARX Neural Networks

Syaffa¹,

Othman²,

Ismahadi³

2018

IJET

View full text Add to dashboard Cite

This paper introduces a Nonlinear Autoregressive Neural Network (NARX) to predict the sensor error of IsFET pH drift with accuracy over the long period. The Bayesian Regularization (BR) backpropagation was used as network training function for this problem and combined with different delay and hidden layer. The results were compared to predict the sensor error in buffer solution pH 4, pH 7 and pH 10 over the time. The NARX performance will be measure based on the value of Mean Squared Error (MSE) and coefficient of determination (R2). The results proved by using Bayesian Regularization with 10 hidden nodes and 50 delays produced the accurate sensor error prediction. This research will provide the significant contributions to the implementation of IsFET pH sensor drift compensation over the time.

show abstract

CMOS Readout Circuit Developments for Ion Sensitive Field Effect Transistor Based Sensor Applications

Cited by 14 publications

References 21 publications

Development of a Portable Multi-Sensor Urine Test and Data Collection Platform for Risk Assessment of Kidney Stone Formation

Development of a Portable Multi-Sensor Urine Test and Data Collection Platform for Risk Assessment of Kidney Stone Formation

Calculation of Electro-Physical Parameters of Ion-Selective Field Effect Transistors: Theoretical and Practical Aspects

Drift Compensation for pH IsFET Sensor Using NARX Neural Networks

Contact Info

Product

Resources

About