Electrical impedance spectroscopy, also called bioimpedance when applied to biological tissues, is an electrical measurement technique which is recently being used to assess food physiological state. Combined with strong data analysis techniques, bioimpedance has the potential to become a non destructive, real-time, low cost, and portable technology for food quality control along the whole supply chain. In this paper, we review recent works focusing on bioimpedance data analysis on food matrix. We briefly describe the traditional statistical methods up to the most recent machine learning solutions The progress achieved and the challenges still to overcome to improve the application are discussed.

Ochratoxin A (OTA) is one of the most widely known and highly toxic types of mycotoxins, able to contaminate different food products and result in serious health problems for consumers. It is therefore important to detect OTA using fast, simple and reliable techniques. In this work, bendable and stretchable selective OTA biosensors were screen-printed on Polyethylene terephthalate (PET) and Poly(dimethylsiloxane) (PDMS), respectively. The working electrodes (WE) were coated with single-walled carbon nanotubes (SWCNTs) and then immobilized with anti-OTA antibodies. The biosensor showed selectivity to OTA compared to another interfering mycotoxin. Furthermore, they were mechanically stable to 5000 cycles of bending down to 7.5 mm diameter, as well as to 300 iterations of stretching up to 33.5%. The presented biosensors have a linear detection range of 0.01 - 1 ng/mL with a limit of detection (LOD) of 0.08 and 0.13 ng/mL in grape juice for the bendable and stretchable biosensor, respectively. These values are one order of magnitude lower than the maximum allowable limits set by the European Union for both grape juice and wine.

We present a portable impedance analyzer based on the AD5933 integrated circuit for fruit quality monitoring. The system, custom designed, allows a flexible tuning of parameters (i.e. excitation voltage, feedback resistor, settling time) in order to allow a precise system calibration over a frequency range from 10 Hz to 100 kHz. The employment of Low-Energy Bluetooth and USB communication, together with a micro SD data storage and a rechargeable battery, increases the system portability and its connectivity especially for precision and smart agriculture applications. The system (7.5×2×4cm in volume), runs MicroPython on an STM32 microcontroller and provides 300 mAh Li-Po battery storage with USB recharge. The developed system is validated by comparing it with a bench-top impedance analyzer, using two-terminal measurement, for the specific analysis of tomatoes and bananas, showing an average Root Mean Square Error (RMSE) as low as 0.6% and 4.1%, for impedance magnitude and phase, respectively.

In this work, we realized a printed immunosensor based on a competitive enzyme-linked immunosorbent assay (ELISA) to allow histamine detection. We investigated the effect of modifying the immunosensor electrodes with spray-deposited poly(3,4-ethyldioxythiophene)poly(styrenesulfunate) (PEDOT:PSS) and single walled carbon nanotube (SWCNT) films. Cyclic voltammetry (CV) was used to characterize the sensor performance in terms of electron transfer kinetics and to prove an enhancement of the electrode active surface area up to 29.1%, due to the use of the additional PEDOT:PSS/SWCNT film. The calibration curve of the so-realized immunosensor (obtained by chronoamperometry measurements) shows a linear detection range of histamine from 0.05 to 50 ng/ml, with 95.03% coefficient of determination.

This paper presents a novel algorithm for the measurement of resistive-type gas sensors with carbon nanotubes (CNTs) as sensitive layer. Contrary to conventional strategies, which extract the sensor information from the normalized resistance, the proposed algorithm is based on the variation in resistance over time. The results have demonstrated that the time necessary to get the maximum performance of these sensors is reduced around a 25% when compared with the conventional approach for any of the recovery strategies analyzed (passive desorption, external heating, or dc voltage). The hardware implementation of the proposed algorithm in a field-programmable gate array (FPGA) has also demonstrated that, in addition to optimizing the sensor performance in terms of time response and sensitivity, this measurement algorithm yields a significant minimization of the sensor readout circuit resources at both software and hardware levels paving the way for future development of smart sensors for the Internet of Things (IoT) applications.

In this contribution, we will present a modeling approach based on a stochastic algorithm that can generate non-rigid solid objects in a three-dimensional space, emulating with high fidelity the typical fabrication processes involved (i.e. spray-coating). A randomly generated set of tube segments and tube junctions is converted into a netlist and the overall electrical behavior is computed via a SPICE software. The theoretical results agree well with experimental data and provide valuable details on the operation mode of the fabricated devices. In addition, we will discuss the characteristics of CNT films prepared by scalable spray deposition as well as inkjet printing onto rigid and flexible substrates. The performance of a variety of devices (e.g. pressure, temperature and gas sensors; FETs) will be described.

The bistable ring physical(ly) unclonable function (BR-PUF) is a novel electrical intrinsic PUF design for physical cryptography. FPGA prototyping has provided a proof-of-concept, showing that the BR-PUF could be a promising candidate for strong PUFs. However, due to the limitations (device resources, placement and routing) of FPGA prototyping, the effectiveness of a practical ASIC implementation of the BR-PUF could not be validated. This paper characterizes the BR-PUF further through transistor-level simulations. Based on process variation, mismatch, and noise models provided or suggested by industry, these simulations are able to provide predictions on the figures-of-merit of ASIC implementations of the BR-PUF. This paper also suggests a more secure way of using the BR-PUF based on its supply voltage sensitivity.

One of the remarkable technical achievements of the past 40 years has been the advances in complexity of the integrated circuit, driven primarily by an exponential rate of reduction in transistor feature sizes, thereby enabling the creation of electronic systems with steadily increasing functionality. The societal benefits in terms of economic growth from Moore's law scaling depend on a knowledge of learning curve cost reductions enabled by Moore's law. It might be possible to increase design efficiency to reduce nonrecurring engineering costs or to develop more cost-effective packaging technologies. The basic physics upon which a transistor operates might be altered 'to utilize other physical phenomena, such as electron spin, magnetic dipoles, photons, etc., to develop new classes of devices that would preserve Moore's law benefits.

In this paper, the historical effects and benefits of Moore's law for semiconductor technologies are reviewed, and it is offered that the rapid learning curve obtained to the benefit of society by feature size scaling might be continued in several different ways. The problem is that as features approach the range of a few nanometers, electron-based devices depart radically from the ideal switch and, in fact, become very leaky in the off state. It is argued that there are some short-term solutions involving more highly parallel manufacturing, increased design efficiency, and lower cost packaging technologies that could continue the steep learning curve for cost reductions that have historically been achieved via Moore's Law scaling. Another alternative might be to increase chip functionality by integrating devices that offer broadened chip functionality including, e.g., sensors, energy sources, oscillators, etc. A third alternative would be to invent an entirely new information processing state variable based on different physics, using electron spin, magnetic dipoles, photons, etc., to improve the performance and reduce switching energy for devices whose smallest features are on the order of a few nanometers. Each of these alternatives is being actively explored and an overview of each strategy and progress to date is given in the paper. A final alternative offered in the paper is to learn from information processing examples in nature, specifically in living systems. An E.coli cell of about one cubic micrometer volume is shown to be an incredibly powerful and energy-efficient information processor relative to the performance of an end-of-scaling silicon processor of the same volume. The paper concludes by pointing out some of the crucial differences between E.coli information processing and conventional approaches with the hope technologies can be invented using the hints offered by biosystems.

The aim of this paper is to show that the multigrid approach can provide an efficient two-dimensional Poisson solver used in the analysis of realistic semiconductor devices based on particle simulators. Our robust implementation of the multigrid method is faster by one or two orders of magnitude than standard successive over-relaxation solvers and is capable, at the same time, of efficiently handling highly inhomogeneous grids and irregular boundary conditions relevant for realistic devices. All essential parts of the algorithm, such as coarsening, prolongation, restriction, and relaxation, have been adapted and optimized to deal with these complex geometries and large variations in the charge density. In particular, a new variant of the Gauss-Seidel-type relaxation scheme is introduced that is particularly suited for grids that lack globally dominant directions. As an example, the multigrid Poisson solver has been applied to two different electronic devices, a GaAs High Electron Mobility Transistor and a Si Metal Oxide Semiconductor Field Effect Transistor.

A critical review of the Monte Carlo (MC) simulation as applied to semiconductor device and process modeling is presented. The general method is discussed, and some special features that can be very useful in particular situations are examined. It is shown that the MC method is a mature technique for modeling and can offer great advantages over more traditional approaches. Critical points are pointed out and analyzed. A variety of applications are then outlined.>

Collocation methods are very useful when one-dimensional Monte Carlo simulations of semiconductor submicron devices require a very accurate solution of Poisson's equation. Potential and electric field may be solved simultaneously with better accuracy than using finite differences. The extension to two dimensions is also outlined. We present the results obtained for Monte Carlo simulation of submicron W/Si and AuGaAs Schottky barrier diodes under forward bias conditions. The accurate solution for the electric field at the ohmic contact boundary allows us to model the injected current and to account for depletion of carriers. Tunnelling effects across the barrier are also included in the simulation.