tinyML EMEA – Prateek Tripathi: Brain Inspired ISFET Arrays – A Tiny ML approch to Lab-on-Chip…

NOTE: He was not presenting in person at EMEA but sent us his recording
Brain Inspired ISFET Arrays – A Tiny ML approch to Lab-on-Chip Diagnostics
PhD Research Student
Imperial College London

Background: Innovation in medical technology plays a significant role in supporting
healthcare. Recent years have witnessed growth in the development of point-of-care devices
that can provide real-time medical diagnostics at the point of need. The COVID-19 pandemic has further highlighted the need for technologies that can provide rapid and accurate diagnosis of infectious diseases without the need for specialized labs. While lateral flow tests have supported mass testing during the pandemic, they suffer from low accuracy and do not allow to multiplex several diseases, which is becoming critical as the pandemic progresses.
Diagnostics can further benefit from the use of Artificial intelligence (AI) which has become a popular tool in the field of healthcare, with medical imaging applications ranging from
diagnostics to assistive surgery giving machines the cognitive ability to make informed

Objective: The aim of this research is to establish new methods for portable and rapid AI-based diagnostics. While most techniques rely on optical methods, electrochemical sensing enables miniaturisation, scalability and robustness. Compatible with CMOS, sensors such as Ion-Sensitive Field-Effect Transistors (ISFETs) can be coupled with novel instrumentation on-chip. We qualify the integration of electrochemical sensing with novel AI algorithms as ‘sensor learning’, leveraging on-chip methodologies to automatically calibrate the sensors and extract accurate diagnostic information. Further to the AI approach, neuromorphic electronics allows to encode the signal in the frequency domain and is inherently compatible with spatial processing at low power. We further intend to use TinyML to bring forth the opportunity to extend deep learning solutions to point-of-care diagnostic devices for infectious diseases. The use of TinyML will alleviate any privacy issues associated with patient data and will also consume significantly lower power than any cloud-based AI solutions.

Methods: Since both neurons and ISFET respond to change in ionic concentration we evaluated four neuromorphic ISFET array topologies involving spatial compensation, temporal integration, linear weighting, and background inhibition for creating low powered spiking ISFET arrays. The arrays have been implemented in TSMC 180nm and take advantage of the fault-tolerant nature, spatial connectivity, spike-domain processing capabilities, neuron inhibitions and AER compatibility of neuromorphic electronics for creating the next generation of LoC devices. We have also created a novel winner-take-all (WTA) architecture for background inhibition in ISFET neurons that can form Clustered WTA and Distributed WTA architecture while at the same time perform drift compensation using temporal and spatial averaging. Finally, we have implemented novel transforms on data collected for COVID-19 and Cancer biomarkers that we trained to classify nucleic acid amplification using convolutional neural networks on microcontrollers and tflite-micro.
Results: This work presents a spatial correlation between the non-ideal effects to facilitate inter-pixel processing using neuromorphic ISFETs. We present four novel ISFET neuron architectures. We begin with neuromorphic ISFET arrays using spike domain encoding and spatial device compensation. This is followed by a completely autonomous cluster topology of neuron-based pixels based on a multiple-channel Integrate and Fire (I&F) architecture for temporal integration and spatial averaging. The designs have been implemented in TSMC 0.18μm. The proposed ISFET topologies are scalable and operate at an ultra-low power of 171.6nW to 410.9nW based on the output spiking frequency. Further, to this, we have established a state-of-the-art with our first models for Lab-on-chip platforms that have been trained to identify infectious diseases and cancer biomarkers using tinyML. This has been done using causal machine learning approaches that have allowed the implementation of novel frameworks for Lab-on-chip platforms.
Conclusion: This work presents four novel neuromorphic architectures for electrochemical sensing. The presented architectures were all implemented in TSMC 0.18μm and work at ultra-low power. In addition, it also presents a framework that can accelerate our testing response to future pandemics using AI at the edge.


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