Imperial College London

Dr Pantelis Georgiou

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Reader in Biomedical Electronics
 
 
 
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Contact

 

+44 (0)20 7594 6326pantelis Website

 
 
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Location

 

902Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
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257 results found

Cacho-Soblechero M, Malpartida-Cardenas K, Moser N, Georgiou Pet al., 2019, Programmable ion-sensing using oscillator-based ISFET architectures, IEEE Sensors Journal, Vol: 19, Pages: 8563-8575, ISSN: 1530-437X

© 2001-2012 IEEE. This paper presents two novel oscillator-based architectures for scalable, programmable, and robust ion sensing using CMOS-based ISFETs. These architectures encode the measured ion concentration in the time domain, generating a pulse width modulated (PWM) signal with a chemically controlled duty cycle. Each architecture is developed for different scenarios and applications. First, the Sawtooth Oscillator front-end addresses the need for robustness and compensation on safety-critical diagnostic platforms. On the other hand, the chemically controlled ring oscillation (CCRO) is ideal for portable point-of-care (PoC) devices, where low power and node scalability are key factors on the system's feasibility, consuming 655 nW during operation. Fabricated in standard CMOS AMS 0.35μ m process, these architectures leverage on the benefits of differential measurements to remain insensitive to temperature, obtaining a relative thermal sensitivity error of 0.0021%/K and 0.0022%/K , respectively, becoming ideal front-ends for the next generation of Lab-on-Chip platforms.

Journal article

Moscardo V, Herrero P, Diez J-L, Gimenez M, Rossetti P, Georgiou P, Bondia Jet al., 2019, Coordinated dual-hormone artificial pancreas with parallel control structure, COMPUTERS & CHEMICAL ENGINEERING, Vol: 128, Pages: 322-328, ISSN: 0098-1354

Journal article

Moser N, Panteli C, Fobelets K, Georgiou Pet al., 2019, Mechanisms for enhancement of sensing performance in CMOS ISFET arrays using reactive ion etching, Sensors and Actuators B: Chemical, Vol: 292, Pages: 297-307, ISSN: 0925-4005

In this work, we investigate the impact of successively removing the passivation layers of ISFET sensors implemented in a standard CMOS process to improve sensing performance. Reactive ion etching is used as a post-processing technique of the CMOS chips for uniform and accurate etching. The removal of the passivation layers addresses common issues with commercial implementation of ISFET sensors, including pH sensitivity, capacitive attenuation, trapped charge, drift and noise. The process for removing the three standard layers (polyimide, Si3N4 and SiO2) is tailored to minimise the surface roughness of the sensing layer throughout an array of more than 4000 ISFET sensors. By careful calibration of the plasma recipe we perform material-wise etch steps at the top and middle of the nitride layer and top of the oxide layer. The characterisation of the ISFET array proves that the location of the trapped charge in the passivation layers is mainly at the interface of the layers. Etching to the top of the oxide layer is shown to induce an improvement of 80% in the offset range throughout the array and an increase in SNR of almost 40 dB compared to the non-processed configuration. The performance enhancement demonstrates the benefit of a controlled industry-standard etch process on CMOS ISFET array system-on-chips.

Journal article

Li K, Liu C, Zhu T, Herrero P, Georgiou Pet al., 2019, GluNet: A deep learning framework for accurate glucose forecasting., IEEE Journal of Biomedical and Health Informatics, Pages: 1-9, ISSN: 2168-2194

For people with Type 1 diabetes (T1D), forecasting of \red{blood glucose (BG)} can be used to effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest continuous glucose monitoring (CGM) technology allows people to observe glucose in real-time. However, an accurate glucose forecast remains a challenge. In this work, we introduce GluNet, a framework that leverages on a personalized deep neural network to predict the probabilistic distribution of short-term (30-60 minutes) future CGM measurements for subjects with T1D based on their historical data including glucose measurements, meal information, insulin doses, and other factors. It adopts the latest deep learning techniques consisting of four components: data pre-processing, label transform/recover, multi-layers of dilated convolution neural network (CNN), and post-processing. The method is evaluated in-silico for both adult and adolescent subjects. The results show significant improvements over existing methods in the literature through a comprehensive comparison in terms of root mean square error (RMSE) (8.88 ± 0.77 mg/dL) with short time lag (0.83 ± 0.40 minutes) for prediction horizons (PH) = 30 mins (minutes), and RMSE (19.90 ± 3.17 mg/dL) with time lag (16.43 ± 4.07 mins) for PH = 60 mins for virtual adult subjects. In addition, GluNet is also tested on two clinical data sets. Results show that it achieves an RMSE (19.28 ± 2.76 mg/dL) with time lag (8.03 ± 4.07 mins) for PH = 30 mins and an RMSE (31.83 ± 3.49 mg/dL) with time lag (17.78 ± 8.00 mins) for PH = 60 mins. These are the best reported results for glucose forecasting when compared with other methods including the neural network for predicting glucose (NNPG), the support vector regression (SVR), the latent variable with exogenous input (LVX), and the auto regression with exogenous input (ARX) algorithm.

Journal article

Cappon, Facchinetti, Sparacino, Georgiou, Herreroet al., 2019, Classification of postprandial glycemic status with application to insulin dosing in type 1 diabetes—an in silico proof-of-concept, Sensors, Vol: 19, Pages: 3168-3168, ISSN: 1424-8220

In the daily management of type 1 diabetes (T1D), determining the correct insulin dose to be injected at meal-time is fundamental to achieve optimal glycemic control. Wearable sensors, such as continuous glucose monitoring (CGM) devices, are instrumental to achieve this purpose. In this paper, we show how CGM data, together with commonly recorded inputs (carbohydrate intake and bolus insulin), can be used to develop an algorithm that allows classifying, at meal-time, the post-prandial glycemic status (i.e., blood glucose concentration being too low, too high, or within target range). Such an outcome can then be used to improve the efficacy of insulin therapy by reducing or increasing the corresponding meal bolus dose. A state-of-the-art T1D simulation environment, including intraday variability and a behavioral model, was used to generate a rich in silico dataset corresponding to 100 subjects over a two-month scenario. Then, an extreme gradient-boosted tree (XGB) algorithm was employed to classify the post-prandial glycemic status. Finally, we demonstrate how the XGB algorithm outcome can be exploited to improve glycemic control in T1D through real-time adjustment of the meal insulin bolus. The proposed XGB algorithm obtained good accuracy at classifying post-prandial glycemic status (AUROC = 0.84 [0.78, 0.87]). Consequently, when used to adjust, in real-time, meal insulin boluses obtained with a bolus calculator, the proposed approach improves glycemic control when compared to the baseline bolus calculator. In particular, percentage time in target [70, 180] mg/dL was improved from 61.98 (± 13.89) to 67.00 (± 11.54; p &lt; 0.01) without increasing hypoglycemia.</jats:p>

Journal article

Georgiou P, Kavousianos X, Cantoro R, Reorda MSet al., 2019, Fault-Independent Test-Generation for Software-Based Self-Testing, Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, Pages: 341-349, ISSN: 1530-4388

Conference paper

Guemes A, Herrero P, Bondia J, Georgiou Pet al., 2019, Modeling the effect of the cephalic phase of insulin secretion on glucose metabolism, MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, Vol: 57, Pages: 1173-1186, ISSN: 0140-0118

Journal article

Liu Y, Constandinou TG, Georgiou P, 2019, A 32×32 ISFET array with in-pixel digitisation and column-Wise TDC for ultra-fast chemical sensing, 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 2158-1525

This paper presents a 32×32 ISFET sensing array with in-pixel digitisation for pH sensing. The in-pixel digitisation is achieved using an inverter-based sensing pixel that is controlled by a triangular waveform. This converts the pH response of the ISFET into a time-domain signal whilst also increasing dynamic range and thus the ability to tolerate sensor offset. The pixels are interfaced to a 15-bit asynchronous column-wise time-to-digital converter (TDC), enabling fast sensor readout whilst using minimal silicon area. Parallel output of 32 TDC interfaces are serialised to achieve fast data though-put. This system is implemented in a standard 0.18 μm standard CMOS technology, with a pixel size of 26 μm × 26 μm and a TDC of 26 μm × 180 μm. Simulation results demonstrate that chemical sampling of up to 5k frames per second can be achieved with a clock frequency of 160 MHz and a TDC resolution of 190 ps. The total power consumption of the overall system is 7.34 mW.

Conference paper

Liu C, Avari P, Leal Y, Wos M, Sivasithamparam K, Georgiou P, Reddy M, Fernández-Real JM, Martin C, Fernández-Balsells M, Oliver N, Herrero Pet al., 2019, A modular safety system for an insulin dose recommender: a feasibility study., Journal of Diabetes Science and Technology, Pages: 1-10, ISSN: 1932-2968

BACKGROUND: Delivering insulin in type 1 diabetes is a challenging, and potentially risky, activity; hence the importance of including safety measures as part of any insulin dosing or recommender system. This work presents and clinically evaluates a modular safety system that is part of an intelligent insulin dose recommender platform developed within the EU-funded PEPPER project. METHODS: The proposed safety system is composed of four modules which use a novel glucose forecasting algorithm. These modules are predictive glucose alerts and alarms; a predictive low-glucose basal insulin suspension module; an advanced rescue carbohydrate recommender for resolving hypoglycemia; and a personalized safety constraint applied to insulin recommendations. The technical feasibility of the proposed safety system was evaluated in a pilot study including eight adult subjects with type 1 diabetes on multiple daily injections over a duration of six weeks. Glycemic control and safety system functioning were compared between the two-weeks run-in period and the end point at eight weeks. A standard insulin bolus calculator was employed to recommend insulin doses. RESULTS: Overall, glycemic control improved over the evaluated period. In particular, percentage time in the hypoglycemia range (<3.0 mmol/l) significantly decreased from 0.82% (0.05-4.79) at run-in to 0.33% (0.00-0.93) at endpoint ( P = .02). This was associated with a significant increase in percentage time in target range (3.9-10.0 mmol/l) from 52.8% (38.3-61.5) to 61.3% (47.5-71.7) ( P = .03). There was also a reduction in number of carbohydrate recommendations. CONCLUSION: A safety system for an insulin dose recommender has been proven to be a viable solution to reduce the number of adverse events associated to glucose control in type 1 diabetes.

Journal article

Malpartida-Cardenas K, Miscourides N, Rodriguez-Manzano J, Yu LS, Baum J, Georgiou Pet al., 2019, Quantitative and rapid Plasmodium falciparum malaria diagnosis and artemisinin-resistance detection using a CMOS Lab-on-Chip platform

<jats:title>Abstract</jats:title><jats:p>Early and accurate diagnosis of malaria and drug-resistance is essential to effective disease management. Available rapid malaria diagnostic tests present limitations in analytical sensitivity, drug-resistant testing and/or quantification. Conversely, diagnostic methods based on nucleic acid amplification stepped forwards owing to their high sensitivity, specificity and robustness. Nevertheless, these methods commonly rely on optical measurements and complex instrumentation which limit their applicability in resource-poor, point-of-care settings. This paper reports the specific, quantitative and fully-electronic detection of <jats:italic>Plas-modium falciparum</jats:italic>, the predominant malaria-causing parasite worldwide, using a Lab-on-Chip platform developed in-house. Furthermore, we demonstrate on-chip detection of C580Y, the most prevalent single-nucleotide polymorphism associated to artemisinin-resistant malaria. Real-time non-optical DNA sensing is facilitated using Ion-Sensitive Field-Effect Transistors, fabricated in unmodified complementary metal-oxide-semiconductor technology, coupled with loop-mediated isothermal amplification. This work holds significant potential for the development of a fully portable and quantitative malaria diagnostic that can be used as a rapid point-of-care test.</jats:p>

Journal article

Moniri A, Rodriguez-Manzano J, Malpartida-Cardenas K, Yu L-S, Didelot X, Holmes A, Georgiou Pet al., 2019, Framework for DNA quantification and outlier detection using multidimensional standard curves, Analytical Chemistry, Vol: 91, Pages: 7426-7434, ISSN: 0003-2700

Real-time PCR is a highly sensitive and powerful technology for the quantification of DNA and has become the method of choice in microbiology, bioengineering, and molecular biology. Currently, the analysis of real-time PCR data is hampered by only considering a single feature of the amplification profile to generate a standard curve. The current “gold standard” is the cycle-threshold (Ct) method which is known to provide poor quantification under inconsistent reaction efficiencies. Multiple single-feature methods have been developed to overcome the limitations of the Ct method; however, there is an unexplored area of combining multiple features in order to benefit from their joint information. Here, we propose a novel framework that combines existing standard curve methods into a multidimensional standard curve. This is achieved by considering multiple features together such that each amplification curve is viewed as a point in a multidimensional space. Contrary to only considering a single-feature, in the multidimensional space, data points do not fall exactly on the standard curve, which enables a similarity measure between amplification curves based on distances between data points. We show that this framework expands the capabilities of standard curves in order to optimize quantification performance, provide a measure of how suitable an amplification curve is for a standard, and thus automatically detect outliers and increase the reliability of quantification. Our aim is to provide an affordable solution to enhance existing diagnostic settings through maximizing the amount of information extracted from conventional instruments.

Journal article

Li K, Daniels J, Liu C, Herrero-Vinas P, Georgiou Pet al., 2019, Convolutional recurrent neural networks for glucose prediction, IEEE Journal of Biomedical and Health Informatics, ISSN: 2168-2194

Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with Type 1 diabetes mellitus (T1DM) such as the artificial pancreas and insulin bolus calculators leverage machine learning techniques for predicting subcutaneous glucose for improved control. Deep learning has recently been applied in healthcare and medical research to achieve state-of-the-art results in a range of tasks including disease diagnosis, and patient state prediction among others. In this work, we present a deep learning model that is capable of forecasting glucose levels with leading accuracy for simulated patient cases (RMSE = 9.38±0.71 [mg/dL] over a 30-minute horizon, RMSE = 18.87±2.25 [mg/dL] over a 60-minute horizon) and real patient cases (RMSE = 21.07±2.35 [mg/dL] for 30-minute, RMSE = 33.27±4.79\% for 60-minute). In addition, the model provides competitive performance in providing effective prediction horizon ( PHeff) with minimal time lag both in a simulated patient dataset ( PHeff = 29.0±0.7 for 30-min and PHeff = 49.8±2.9 for 60-min) and in a real patient dataset ( PHeff = 19.3±3.1 for 30-min and PHeff = 29.3±9.4 for 60-min). This approach is evaluated on a dataset of 10 simulated cases generated from the UVa/Padova simulator and a clinical dataset of 10 real cases each containing glucose readings, insulin bolus, and meal (carbohydrate) data. Performance of the recurrent convolutional neural network is benchmarked against four algorithms. The proposed algorithm is implemented on an Android mobile phone, with an execution time of 6ms on a phone compared to an execution time of 780ms on a laptop.

Journal article

Rawson TM, Hernandez B, Moore L, Blandy O, Herrero P, Gilchrist M, Gordon A, Toumazou C, Sriskandan S, Georgiou P, Holmes Aet al., 2019, Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study, Journal of Antimicrobial Chemotherapy, Vol: 74, Pages: 1108-1115, ISSN: 0305-7453

BackgroundInfection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood parameters on presentation to hospital.MethodsAn SML algorithm was developed to classify cases into infection versus no infection using microbiology records and six available blood parameters (C-reactive protein, white cell count, bilirubin, creatinine, ALT and alkaline phosphatase) from 160 203 individuals. A cohort of patients admitted to hospital over a 6 month period had their admission blood parameters prospectively inputted into the SML algorithm. They were prospectively followed up from admission to classify those who fulfilled clinical case criteria for a community-acquired bacterial infection within 72 h of admission using a pre-determined definition. Predictive ability was assessed using receiver operating characteristics (ROC) with cut-off values for optimal sensitivity and specificity explored.ResultsOne hundred and four individuals were included prospectively. The median (range) cohort age was 65 (21–98)  years. The majority were female (56/104; 54%). Thirty-six (35%) were diagnosed with infection in the first 72 h of admission. Overall, 44/104 (42%) individuals had microbiological investigations performed. Treatment was prescribed for 33/36 (92%) of infected individuals and 4/68 (6%) of those with no identifiable bacterial infection. Mean (SD) likelihood estimates for those with and without infection were significantly different. The infection group had a likelihood of 0.80 (0.09) and the non-infection group 0.50 (0.29) (P < 0.01; 95% CI: 0.20–0.40). ROC AUC was 0.84 (95% CI: 0.76–0.91).ConclusionsAn SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.

Journal article

Rawson TM, Ahmad R, Toumazou C, Georgiou P, Holmes Aet al., 2019, Artificial intelligence can improve decision-making in infection management, Nature Human Behaviour, ISSN: 2397-3374

Antibiotic resistance is an emerging global danger. Reaching responsible prescribing decisions requires the integration of broad and complex information. Artificial intelligence tools could support decision-making at multiple levels, but building them needs a transparent co-development approach to ensure their adoption upon implementation.

Journal article

Ming D, Rawson T, Sangkaew S, Rodriguez-Manzano J, Georgiou P, Holmes Aet al., 2019, Connectivity of rapid-testing diagnostics and surveillance of infectious diseases (vol 97, pg 244, 2019), BULLETIN OF THE WORLD HEALTH ORGANIZATION, Vol: 97, ISSN: 0042-9686

Journal article

Ming D, Rawson T, Sangkaew S, Rodriguez-Manzano J, Georgiou P, Holmes Aet al., 2019, Connectivity of rapid-testing diagnostics and surveillance of infectious diseases, Bulletin of the World Health Organization, Vol: 97, Pages: 242-244, ISSN: 0042-9686

The World Health Organization (WHO) developed the ASSURED criteria to describe the ideal characteristics for point-of-care testing in low-resource settings: affordable, sensitive, specific, user-friendly, rapid and robust, equipment-free and deliverable.1 These standards describe. Over the last decade, widespread adoption of point-of-care testing has led to significant changes in clinical decision-making processes. The development of compact molecular diagnostics, such as the GeneXpert® platform, have enabled short turnaround times and allowed profiling of antimicrobial resistance. Although modern assays have increased operational requirements, many devices are robust and can be operated within communities with minimal training. These new generation of rapid tests have bypassed barriers to care and enabled treatment to take place independently from central facilities. Here we describe the importance of connectivity, the automatic capture and sharing of patient healthcare data from testing, in the adoption and roll-out of rapid testing.

Journal article

Miscourides N, Georgiou P, 2019, ISFET Arrays in CMOS: A Head-to-Head Comparison Between Voltage and Current Mode, IEEE SENSORS JOURNAL, Vol: 19, Pages: 1224-1238, ISSN: 1530-437X

Journal article

Rodriguez-Manzano J, Moniri A, Malpartida-Cardenas K, Dronavalli J, Davies F, Holmes A, Georgiou Pet al., 2019, Simultaneous single-channel multiplexing and quantification of carbapenem-resistant genes using multidimensional standard curves, Analytical Chemistry, Vol: 91, Pages: 2013-2020, ISSN: 0003-2700

Multiplexing and quantification of nucleic acids, both have, in their own right, significant and extensive use in biomedical related fields. Currently, the ability to detect several nucleic acid targets in a single-reaction scales linearly with the number of targets; an expensive and time-consuming feat. Here, we propose a new methodology based on multidimensional standard curves that extends the use of real-time PCR data obtained by common qPCR instruments. By applying this novel method-ology, we achieve simultaneous single-channel multiplexing and enhanced quantification of multiple targets using only real-time amplification data. This is obtained without the need of fluorescent probes, agarose gels, melting curves or sequencing analysis. Given the importance and demand for tackling challenges in antimicrobial resistance, the proposed method is ap-plied to four of the most prominent carbapenem-resistant genes: blaOXA-48, blaNDM, blaVIM and blaKPC, which account for 97% of the UK's reported carbapenemase-producing Enterobacteriaceae.

Journal article

Gonzalez AG, Herrero P, Georgiou P, 2019, A CONTROLLER FOR BLOOD GLUCOSE REGULATION BASED ON MODULATION OF INSULIN SENSITIVITY IN PEOPLE WITH TYPE I DIABETES, Publisher: MARY ANN LIEBERT, INC, Pages: A48-A48, ISSN: 1520-9156

Conference paper

Herrero P, Reddy M, Georgiou P, Oliver Net al., 2019, A BETTER CARE FOR DIABETES, Publisher: MARY ANN LIEBERT, INC, Pages: A7-A7, ISSN: 1520-9156

Conference paper

Li K, Chen J, Herrero P, Uduku C, Georgiou Pet al., 2019, A DEEP NEURAL NETWORK PLATFORM FOR PREDICTING BLOOD GLUCOSE LEVELS, Publisher: MARY ANN LIEBERT, INC, Pages: A80-A80, ISSN: 1520-9156

Conference paper

Uduku C, Li K, Daniiels J, Hererro P, Reddy M, Oliver N, Spence R, Georgiou Pet al., 2019, DEVELOPMENT OF AN ADAPTIVE, REAL-TIME, INTELLIGENT SYSTEM TO ENHANCE SELF-CARE OF CHRONIC DISEASE (ARISES), Publisher: MARY ANN LIEBERT, INC, Pages: A69-A69, ISSN: 1520-9156

Conference paper

Liu C, Avari PE, Oliver N, Georgiou P, Vinas PHet al., 2019, COORDINATING LOW-GLUCOSE INSULIN SUSPENSION AND CARBOHYDRATE RECOMMENDATIONS FOR HYPOGLYCAEMIA MINIMISATION, Publisher: MARY ANN LIEBERT, INC, Pages: A85-A85, ISSN: 1520-9156

Conference paper

Güemes A, Herrero P, Georgiou P, 2019, A novel glucose controller using insulin sensitivity modulation for management of type 1 diabetes, ISSN: 0271-4310

© 2019 IEEE This paper introduces the use of bioelectronic medicine for glucose control in Type 1 diabetes. In particular, we present a new hybrid closed-loop glucose controller that regulates (i) the insulin and glucagon doses delivered using a pump and (ii) the value of insulin sensitivity of the patient, which would be modulated through electrical stimulation of the nervous system. The presented controller achieves improved glucose control with increased percentage of time of glucose levels within target and decreased hormonal delivery when compared with conventional glucose controllers. This work shows the potential of using bioelectronic modulation of insulin sensitivity for diabetes management.

Conference paper

Zeng J, Georgiou P, 2019, Current-mode ISFET array with row-parallel ADCs for ultra-high speed ion imaging, ISSN: 0271-4310

© 2019 IEEE This paper presents a fully integrated system-on-chip for ultra-high frame rate ion-imaging using a pH-sensing ISFET array. Linear pH-to-current conversion is achieved by operating the ISFET in velocity saturation which guarantees that the ion concentration in the chemical solution is linearly transduced to the output of the sensor. Implemented in a 3-Transistor (3-T) pixel for compactness, the ISFET also consists of a reset switch to compensate for sensor non-ideal effects such as trapped charge and drift. High speed readout is achieved using a current-mode signal processing pipeline while auto-zeroing is employed to reduce fixed pattern noise. The sensing array comprises 128 × 128 pixels and every row shares its own readout circuit followed by 128 row-parallel 1MS/sec single slope ADCs. Designed in standard TSMC 180nm CMOS process, the chip achieves 7800fps with 16k pixels and a silicon area of 2mm × 2mm, which is the fastest ISFET array reported in literature.

Conference paper

Tripathi P, Moser N, Georgiou P, 2019, A neuron-based ISFET array architecture with spatial sensor compensation, ISSN: 0271-4310

© 2019 IEEE We present the next step of neuromorphic ISFET arrays with spike domain encoding and spatial device compensation. Each pixel provides a spiking signal with a frequency related to the pH in solution and expected sensitivity of 48.6 to 112.2 kHzldpH. The array is arranged as clusters which use regulation to cancel undesirable sensor offset and then linear interpolation for temporal drift during the readout. The scheme relies on spatial correlation of ISFET behaviour which is demonstrated with a low standard deviation of 11.6 mV sensor offset, which is well in the pixel compensation range of ± 500 mV. On an array level, address-event representation is used for external signal handling, which enables low power and scalable throughput. The array chip is implemented in TSMC 0.18 urn CMOS technology.

Conference paper

Karolcik S, Miscourides N, Georgiou P, 2019, Live demonstration: A portable high-speed ion-imaging platform using a raspberry Pi, ISSN: 0271-4310

© 2019 IEEE A portable and low-cost system capable of high-speed ion-imaging using ISFET arrays will be demonstrated. The system takes advantage of the portability and processing power of Raspberry Pi, enabling high-speed measurements from a large-scale ISFET array. This array contains 64x200 ISFET pixels and is connected to a custom PCB that provides analogue-to-digital conversion as well as configuration signals. This platform offers a complete solution allowing ion-imaging and pH observations outside of the lab environment. We anticipate that this efficient approach holds significant potential for affordable ion-imaging systems, making them more readily available for further research.

Conference paper

Miscourides N, Georgiou P, 2019, Mismatch compensation in ISFET arrays using a parasitic programmable gate, ISSN: 0271-4310

© 2019 IEEE In this paper, we show a compensation method for mismatch in large-scale ISFET arrays which is caused by the presence of trapped charge at the sensor's floating gate. To facilitate ISFET calibration, the Programmable-Gate method is used. We improve on a previously proposed gradient descent algorithm by making an a priori estimate of the calibration step size thus allowing to reduce the number of iterations to one. This is enabled by an initial characterisation of the programmable gate capacitor in order to determine the effect of the calibration signal on the pixel's output. Measured results of both approaches are presented using a 64×200 ISFET array with a parasitic PG capacitor located vertically inside the pixel stack such that pixel area is not compromised. Additionally, results are shown for two chips which correspond to different trapped charge spreads with both algorithms reducing the standard deviation of the trapped charge by an average of 78% and 66% respectively.

Conference paper

Rawson T, Ming D, Gowers S, Freeman D, Herrero P, Georgiou P, Cass AEG, O'Hare D, Holmes Aet al., 2019, Public acceptability of computer-controlled antibiotic management: an exploration of automated dosing and opportunities for implementation, Journal of Infection, Vol: 78, Pages: 75-86, ISSN: 0163-4453

Journal article

Cacho-Soblechero M, Georgiou P, 2019, A programmable, highly linear and PVT-insensitive ISFET array for PoC diagnosis, ISSN: 0271-4310

© 2019 IEEE This paper presents a novel 32x32 ISFET array for DNA amplification detection. Each pixel contains a highly-linear ISFET-based OTA and a sawtooth oscillator, converting the solution pH into a digital clock with chemically controlled duty cycle. By employing a differential measurement between a DAC-generated voltage and the pH solution on the OTA, a highly linear, PVT-insensitive response is achieved, while opening the possibility of real-time pixel-wise compensation of trapped charge and chemical drift. The proposed architecture achieves a sensitivity of 11.78%/pH while maintaining large linear dynamic range and a temperature sensitivity of 0.0033 pH/K. Implemented using 0.18µm standard CMOS technology, each pixel occupies 40 µm x 40 µm. This architecture paves the way towards a new generation of ISFET imagers, capable of learning from data and correcting their measurements in real time.

Conference paper

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