A CMOS Imager with PFM/PWM Based Analog-to-digital Converter

An on-pixel analog-to-digital converter based on both PFM and PWM schemes is reported. The proposed architecture uses a limited number of transistors that can be implemented in a small silicon area resulting in a 23% fill-factor. The digital sensor can be externally configured in order to operate under either the PFM or PWM scheme. At high light intensities, the PFM scheme is replaced by the PWM scheme which proves to be much more efficient in terms of power consumption and clock frequency requirements. An in-built light adaptation mechanism has also been implemented which allows the sensor to better adapt to low-light intensity or to adjust the sensor saturation level. As a consequence, the sensor features a programmable dynamic range. Image lag is reduced in both schemes since a reset of the photodetector is performed after the conversion period. The pixel based ADC has been designed and fabricated using CMOS 0.25 μm technology

Quantum Parametric Amplification and NonClassical Correlations due to 45 nm nMOS Circuitry Effect

This study unveils a groundbreaking exploration of using semiconductor technology in quantum circuitry. Leveraging the unique operability of 45 nm CMOS technology at deep cryogenic temperatures (~ 300 mK), a novel quantum electronic circuit is meticulously designed. Through the intricate coupling of two matching circuits via a 45 nm nMOS transistor, operating as an open quantum system, the circuit quantum Hamiltonian and the related Heisenberg-Langevin equation are derived, setting the stage for a comprehensive quantum analysis. Central to this investigation are three pivotal coefficients derived, which are the coupling between the coupled oscillator charge and flux operators through the internal circuit of the transistor. These coefficients emerge as critical determinants, shaping both the circuit potential as a parametric amplifier and its impact on quantum properties. The study unfolds a delicate interplay between these coefficients, showcasing their profound influence on quantum discord and the gain of the parametric amplifier. Consequently, the assimilation of 45 nm CMOS technology with quantum circuitry makes it possible to potentially bridge the technological gap in quantum computing applications, where the parametric amplifier is a necessary and critical device. The designed novel quantum device serves not only as a quantum parametric amplifier to amplify quantum signals but also enhances the inherent quantum properties of the signals such as non-classicality. Therefore, one can create an effective parametric amplifier that simultaneously improves the quantum characteristics of the signals. The more interesting result is that if such a theory becomes applicable, the circuit implemented in the deep-cryogenic temperature can be easily compatible with the next step of circuitry while keeping the same electronic features compatibility with the quantum processor.Comment: 11 pages, 5 figure

A compact multi-chip-module implementation of a multi-precision neural network classifier

This paper describes a novel MCM digital implementation of a reconfigurable multi-precision neural network classifier. The design is based on a scalable systolic architecture with a user defined topology and arithmetic precision of the neural network. Indeed, the MCM integrates 64/32/16 neurons with a corresponding accuracy of 4/8/16-bits. A prototype has been designed and successfully tested in CMOS 0.7 μm technolog

Smart Manufacturing Technologies for Printed Electronics

Fabrication of electronic devices on different flexible substrates is an area of significant interest due to low cost, ease of fabrication, and manufacturing at ambient conditions over large areas. Over the time, a number of printing technologies have been developed to fabricate a wide range of electronic devices on nonconventional substrates according to the targeted applications. As an increasing interest of electronic industry in printed electronics, further expansion of printed technologies is expected in near future to meet the challenges of the field in terms of scalability, yield, and diversity and biocompatibility. This chapter presents a comprehensive review of various printing electronic technologies commonly used in the fabrication of electronic devices, circuits, and systems. The different printing techniques based on contact/noncontact approach of the printing tools with the target substrates have been explored. These techniques are assessed on the basis of ease of operation, printing resolutions, processability of materials, and ease of optimization of printed structures. The various technical challenges in printing techniques, their solutions with possible alternatives, and the potential research directions are highlighted. The latest developments in assembling various printing tools for enabling high speed and batch manufacturing through roll-to-roll systems are also explored

A wide dynamic range cmos imager with extended shunting inhibition image processing capabilities

A CMOS imager based on a novel mixed-mode VLSI implementation of biologically inspired shunting inhibition vision models is presented. It can achieve a wide range of image processing tasks such as image enhancement or edge detection via a programmable shunting inhibition processor. Its most important feature is a gain control mechanism allowing local and global adaptation to the mean input light intensity. This feature is shown to be very suitable for wide dynamic range imager

FPGA implementation of a predictive vector quantization image compression algorithm for image sensor applications

This paper presents a hybrid image compression scheme based on a block based compression algorithm referred to as Vector Quantization (VQ) combined with the Differential Pulse Code Modulation (DPCM) technique. The proposed image compression technique called the PVQ scheme results in enhanced image quality as compared to the standalone VQ. The generated codebooks for the PVQ scheme are more robust for image coding than that of the VQ. This made our system a suitable candidate for developing on chip image sensor with integrated data compression processor. The proposed system was validated through FPGA implementation. The resulting implementation achieved good compression and image quality with moderate system complexity

Image segmentation using Spiking Pixel Architecture

Colloque avec actes et comité de lecture. internationale.International audienceThis paper describes a spiking pixel architecture that can be used in a locally interconnected network in order to perform image capture as well as image segmentation. In this network, interaction between pixels can be locally obtained and is mediated via the discharge speed of each spiking pixel. The paper studies three different architecture of locally coupled pixels and shows that synchronisation of two coupled pixels can be obtained using only local excitation schemes. A theoretical condition for synchronisation was also deduced and which confirms the obtained simulation results. The proposed schemes were also successfully tested for a set of noisy gray scale images

An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

Abstract: Increasing the number of sensors in a gas identification system generally improves its performance as this will add extra features for analysis. However, this affects the computational complexity, especially if the identification algorithm is to be implemented on a hardware platform. Therefore, feature reduction is required to extract the most important information from the sensors for processing. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA)-based feature reduction algorithms have been analyzed using the data obtained from two different types of gas sensors, i.e., seven commercial Figaro sensors and in-house fabricated 4×4 tin-oxide gas array sensor. A decision tree-based classifier is used to examine the performance of both the PCA and LDA approaches. The software implementation is carried out in MATLAB and the hardware implementation is performed using the Zynq system-on-chip (SoC) platform. It has been found that with the 4×4 array sensor, two discriminant functions (DF) of LDA provide 3.3% better classification than five PCA components, while for the seven Figaro sensors, two principal components and one DF show the same performances. The hardware implementation results on the programmable logic of the Zynq SoC shows that LDA outperforms PCA by using 50% less resources as well as by being 11% faster with a maximum running frequency of 122 MHz

A Spiking Neural Network for Gas Discrimination using a Tin Oxide Sensor Array

International audienceWe propose a bio-inspired signal processing method for odor discrimination. A spiking neural network is trained with a supervised learning rule so as to classify the analog outputs from a monolithic 4×4 tin oxide gas sensor array implemented in our in-house 5 µm process. This scheme has been sucessfully tested on a discrimination task between 4 gases (hydrogen, ethanol, carbon monoxide, methane). Performance compares favorably to the one obtained with a common statistical classifier. Moreover, the simplicity of our method makes it well suited for building dedicated hardware for processing data from gas sensor arrays

A 4×4 Logarithmic Spike Timing Encoding Scheme for Olfactory Sensor Applications

International audienceThis paper presents a 4×4 logarithmic spike-timing encoding scheme used to translate the output of an integrated tin oxide gas sensor array into spike sequence, which is exploited to perform gas recognition. Hydrogen, Ethanol and Carbon monoxide were used to characterize the gas sensor array. The collected data were then used to test the proposed circuit for spike encoding and gas recognition. Simulation results illustrate that a particular analyte gas generates a unique spike pattern with certain spike ordering sequence, which is independent of the gas concentration. This unique spike sequence can thus be used to recognize different gases. In addition, the concentration information can also be extracted from the time-to-the-first spike in the sequence making it possible to perform not only gas/odor recognition but quantification as well