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Proceedings ArticleDOI

Neuromorphic Approach based Current Sensing Analog to Digital Converter for Biomedical Applications

TL;DR: In this paper, a leaky integrate and fire (LIF) neuron model inspired from neuromorphic circuits along with digital control circuits and error correction circuit is proposed to enhance INL/DNL performance.
Abstract: A current sensing analog-to-digital converter (CADC) targeting biomedical applications is proposed in this paper. The proposed architecture consists of a leaky integrate and fire (LIF) neuron model inspired from neuromorphic circuits along with digital control circuits and error correction circuit to enhance INL/DNL performance. The architecture is tuned for input signal ranging from 1 µA to 64 µA with power supply of 1/1.8 V for digital and analog blocks respectively. The design is implemented in 0.18 µm CMOS 1P4M triple-well process of Tower Jazz Semiconductor's technology. Total power consumption of the circuit is 1. 95 mW & 48.86 μW respectively and achieves FoM of 331.6 & 71.24 pJ/conversion-step for the cases with and without error correction for 6-bits operation.
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Journal ArticleDOI
TL;DR: A novel low‐power, tuneable resolution and low‐current sensing analog‐to‐digital converter is proposed in this paper that consists of silicon neurons along with digital circuits.

2 citations

Journal ArticleDOI
06 Jun 2023-Chips
TL;DR: In this paper , the authors present the measurement outcomes of the SA-SRC on-chip, evaluating the efficacy of its adaptation scheme, and assessing its capability to produce spike orders that correspond to the temporal difference between the two spikes received at its inputs.
Abstract: In contemporary devices, the number and diversity of sensors is increasing, thus, requiring both efficient and robust interfacing to the sensors. Implementing the interfacing systems in advanced integration technologies faces numerous issues due to manufacturing deviations, signal swings, noise, etc. The interface sensor designers escape to the time domain and digital design techniques to handle these challenges. Biology gives examples of efficient machines that have vastly outperformed conventional technology. This work pursues a neuromorphic spiking sensory system design with the same efficient style as biology. Our chip, that comprises the essential elements of the adaptive neuromorphic spiking sensory system, such as the neuron, synapse, adaptive coincidence detection (ACD), and self-adaptive spike-to-rank coding (SA-SRC), was manufactured in XFAB CMOS 0.35 μm technology via EUROPRACTICE. The main emphasis of this paper is to present the measurement outcomes of the SA-SRC on-chip, evaluating the efficacy of its adaptation scheme, and assessing its capability to produce spike orders that correspond to the temporal difference between the two spikes received at its inputs. The SA-SRC plays a crucial role in performing the primary function of the adaptive neuromorphic spiking sensory system. The measurement results of the chip confirm the simulation results of our previous work.
References
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Journal ArticleDOI
TL;DR: A nanopore-based device provides single-molecule detection and analytical capabilities that are achieved by electrophoretically driving molecules in solution through a nano-scale pore, a unique analytical capability that makes inexpensive, rapid DNA sequencing a possibility.
Abstract: A nanopore-based device provides single-molecule detection and analytical capabilities that are achieved by electrophoretically driving molecules in solution through a nano-scale pore. The nanopore provides a highly confined space within which single nucleic acid polymers can be analyzed at high throughput by one of a variety of means, and the perfect processivity that can be enforced in a narrow pore ensures that the native order of the nucleobases in a polynucleotide is reflected in the sequence of signals that is detected. Kilobase length polymers (single-stranded genomic DNA or RNA) or small molecules (e.g., nucleosides) can be identified and characterized without amplification or labeling, a unique analytical capability that makes inexpensive, rapid DNA sequencing a possibility. Further research and development to overcome current challenges to nanopore identification of each successive nucleotide in a DNA strand offers the prospect of 'third generation' instruments that will sequence a diploid mammalian genome for ∼$1,000 in ∼24 h.

2,512 citations

Journal ArticleDOI
TL;DR: To provide a comprehensive understanding of the field effect in silicon nanowire (SiNW) sensors, a systematic approach to fine tune the distance of a charge layer by controlling the hybridization sites of DNA to the SiNW preimmobilized with peptide nucleic acid (PNA) capture probes.
Abstract: To provide a comprehensive understanding of the field effect in silicon nanowire (SiNW) sensors, we take a systematic approach to fine tune the distance of a charge layer by controlling the hybridization sites of DNA to the SiNW preimmobilized with peptide nucleic acid (PNA) capture probes. Six target DNAs of the same length, but differentiated successively by three bases in the complementary segment, are hybridized to the PNA. Fluorescent images show that the hybridization occurs exclusively on the SiNW surface between the target DNAs and the PNA. However, the field-effect response of the SiNW sensor decreases as the DNA (charge layer) moves away from the SiNW surface. Theoretical analysis shows that the field effect of the SiNW sensor relies primarily on the location of the charge layer. A maximum of 102% change in resistance is estimated based on the shortest distance of the DNA charge layer (4.7 A) to the SiNW surface.

286 citations

Journal ArticleDOI
TL;DR: A brief history of neuromorphic engineering is presented, some of the principal current large-scale projects, their main features, how their approaches are complementary and distinct, their advantages and drawbacks, and highlight the sorts of capabilities that each can deliver to neural modellers are highlighted.
Abstract: Neuromorphic computing covers a diverse range of approaches to information processing all of which demonstrate some degree of neurobiological inspiration that differentiates them from mainstream conventional computing systems. The philosophy behind neuromorphic computing has its origins in the seminal work carried out by Carver Mead at Caltech in the late 1980s. This early work influenced others to carry developments forward, and advances in VLSI technology supported steady growth in the scale and capability of neuromorphic devices. Recently, a number of large-scale neuromorphic projects have emerged, taking the approach to unprecedented scales and capabilities. These large-scale projects are associated with major new funding initiatives for brain-related research, creating a sense that the time and circumstances are right for progress in our understanding of information processing in the brain. In this review we present a brief history of neuromorphic engineering then focus on some of the principal current large-scale projects, their main features, how their approaches are complementary and distinct, their advantages and drawbacks, and highlight the sorts of capabilities that each can deliver to neural modellers.

275 citations

Proceedings ArticleDOI
25 May 2003
TL;DR: A low-power analog circuit for implementing a model of a leaky integrate and fire neuron that includes elements for implementing spike frequency adaptation, for setting an arbitrary refractory period and for modulating the neuron's threshold voltage is presented.
Abstract: We present a low-power analog circuit for implementing a model of a leaky integrate and fire neuron. Next to being optimized for low-power consumption, the proposed circuit includes elements for implementing spike frequency adaptation, for setting an arbitrary refractory period and for modulating the neuron's threshold voltage. We present experimental data from a prototype chip, implemented using a standard 1.5 /spl mu/m CMOS process.

253 citations

Journal ArticleDOI
TL;DR: An architecture and implementation of a multichannel potentiostat array based on a novel semi-synchronous sigma-delta (SigmaDelta) analog-to-digital conversion algorithm that combines continuous time SigmaDelta with time-encoding machines, and enables measurement of currents down to femtoampere range is presented.
Abstract: Rapid and accurate detection of pathogens using conductometric biosensors requires potentiostats that can measure small variations in conductance. In this paper, we present an architecture and implementation of a multichannel potentiostat array based on a novel semi-synchronous sigma-delta (SigmaDelta) analog-to-digital conversion algorithm. The algorithm combines continuous time SigmaDelta with time-encoding machines, and enables measurement of currents down to femtoampere range. A 3-mmtimes3-mm chip implementing a 42-channel potentiostat array has been prototyped in a 0.5-mum CMOS technology. Measured results demonstrate that the prototype can achieve 10 bits of resolution, with a sensitivity down to 50-fA current. The power consumption of the potentiostat has been measured to be 11 muW per channel for a sampling rate of 250 kHz. Experiments with a conductometric biosensor specific to Bacillus Cereus bacterium, demonstrate the ability of the potentiostat in identifying different concentration levels of the pathogen in a biological sample

106 citations