Kian Jafari

Artificial intelligence systems are increasingly embedded in daily life, but they face area and power constraints. Consequently, alternatives to binary architectures are being pursued. Ternary logic is one of those solutions that offers increased data density and enhanced computational speed. However, area overhead remains a major barrier to low‐cost, energy‐efficient ternary systems. To address these challenges, we design compact and energy‐efficient ternary logic gates based on the simulated negative differential resistance behavior of an experimentally informed ferroelectric carbon nanotube field‐effect transistor (FeCNTFET) compact model. A 10nm ferroelectric thin film on (001) was experimentally fabricated and characterized, and the extracted ferroelectric parameters were incorporated into a Landau–Khalatnikov submodel coupled to a 3nm CNTFET compact model calibrated against previously reported experimental CNTFET data. Our results indicate that by using the compact ternary FeCNTFET‐based arithmetic units, the proposed Ternary neural network (TNN) accelerators require 85% fewer unit‐sized transistors and consume 72% less power than the 8‐bit integer implementation, with accuracy reductions of 3.1% and 5.1% for MNIST and CIFAR‐10, respectively. These results indicate the potential of the proposed FeCNTFET‐based TNN accelerator for energy‐ and resource‐constrained applications.

This article presents an ultrasensitive surface stress-based BioMEMS platform with an optical biosensing detection method. The proposed biosensor consists of two main parts: a microelectromechanical systems (MEMS) transducer, which converts the chemical interaction of the bioreceptors with the target bioparticles into mechanical displacement, and an optical system to detect the displacement of the MEMS transducer and determine the concentration of the target bioparticles. This design uses a membrane held by six stands above a waveguide as the MEMS transducer to capture the target bioparticles in the test sample. The absorption of the target bioparticles by the bioreceptors, which are immobilized on the surface of the movable membrane, creates surface stress on the top surface of the membrane, leading to its deformation. While the movable part approaches the waveguide, it interacts with the modes’ evanescent field, increasing the effective refractive index. Finally, the refractive index variation causes a shift in the mode’s phase that determines the concentration of the target bioparticles. The operational characteristics of the present biosensor resulting from numerical and analytical approaches are as follows: phase shift of 250π, optical sensitivity of 1935π rad/RIU, mechanical sensitivity of 1.64 μm/N⋅m^-1, and figure of merit (FOM) of 1.29 πrad/RIUμm. The obtained results indicate that the proposed biosensor has the potential to be employed in point-of-care (POC) tests. This would enable the detection of target biomolecules associated with specific diseases and the measurement of their concentrations, which is indicative of disease progression.

This paper presents a novel approach for implementing Binary Neural Networks (BNNs) utilizing nonvolatile optical phase shifters. These phase shifters employ a micro-electromechanical system (MEMS) tuning mechanism, which enables the adjustment of the refractive index and phase of the propagating mode. In this approach, the weights of the BNN can be controlled by applying electrical signals to the phase shifters. Moreover, due to the nonvolatile operation of these devices, the network’s weights remain stable even when the electrical power source is cut off. The phases of the propagating modes, manipulated by the proposed phase shifters, determine the logic of the photonic circuit. The in-memory design of this device eliminates the need for network register banks, thereby significantly reducing resource usage, footprint, and power consumption. This approach offers much faster operation than other technologies, such as CMOS or spintronics, making it particularly appealing for edge artificial intelligence applications.

Contrast change is an important factor that affects the quality of images. During image capturing, unfavorable lighting conditions can cause contrast change and visual quality loss. While various methods have been proposed to assess the quality of images under different distortions such as blur and noise, contrast distortion has been largely overlooked as its visual impact and properties are different from other conventional types of distortions. In this paper, we propose a no-reference image quality assessment (NR-IQA) metric for contrast-distorted images. Using a set of contrast enhancement algorithms, we aim to generate pseudo-reference images that are visually close to the actual reference image, such that the NR problem is transformed to a Full-reference (FR) assessment with higher accuracy. To this end, a large dataset of contrast-enhanced images is produced to train a classification network that can select the most suitable contrast enhancement algorithm – based on image content and distortion – for pseudo-reference image generation. Finally, the evaluation is performed in the FR manner to assess the quality difference between the contrast-enhanced (pseudo-reference) and degraded images. Performance evaluation of the proposed method on three databases containing contrast distortions (CCID2014, TID2013, and CSIQ), indicates the promising performance of the proposed method.

This paper presents a novel optomechanical surface stress-based biosensor. The proposed device employs a functionalized microelectromechanical systems (MEMS) transducer to capture the target bioparticles in the sample. The interaction of the bioreceptors immobilized on the transducer surface with the target bioparticles modifies the transducer’s surface stress which results in its deformation. Accurate measurement of the transducer deformation allows for label-free detection of the target bioparticles and their concentration. In order to measure the transducer deformation, an optical system consisting of a defective one-dimensional photonic crystal with adjustable defect width is utilized, which significantly enhances the sensor sensitivity. This BioMEMS platform exhibits improved functional characteristics, including a bandwidth of 5kHz, FWHM of 0.8nm, quality factor of 1822.5, mechanical sensitivity of 13.5μm/Nm^-1, optical sensitivity of 1.02, and total sensitivity of 13.775μm/Nm-1. These functional characteristics demonstrate the capability of the proposed sensor to detect various diseases in their early stages or progression, which is highly desirable in Point-of-Care testing (POCT) applications.

Signed multiplication is crucial for performing arithmetic operations with positive and negative numbers. It has applications in signal processing, digital image processing, communication, cryptography, neural network hardware accelerators, and more. Until today, to multiply signed numbers, the data are often converted to unsigned format, and the sign is added to the final product. This method imposes high hardware requirements for data conversion and presents challenges in designing approximate multipliers. This paper proposes a novel method for designing approximate signed multipliers that eliminate the need for data conversion, thereby reducing the overall hardware requirement while maintaining accuracy. Comprehensive evaluations at the system level using MATLAB and circuit-level performance analysis using HSPICE demonstrate that the proposed approach offers a successful trade-off between area overhead, power consumption, and accuracy. The achieved trade-off makes the proposed method a promising solution for optimizing digital circuits in various applications such as artificial intelligence and image processing.