The recent growth of Artificial Neural Networks fueled the design of numerous Artificial Intelligence (AI) dedicated hardware implementations. High power dissipation, computational complexity, and large area footprints currently limit CMOS based real-time embedded AI applications. In this work, we design and simulate through SPICE, for the first time, an artificial analog neuron based on the molecular Field-Effect Transistor (molFET) technology. MolFETs are described by a circuital model whose physical characteristics are extracted from atomistic simulations. The designed neuron is a single column of a crossbar-like circuit representing a layer of seven parallel neurons. The drain currents sum up in a soma-like circuit - modelled through a comparator - and trigger the output pulses. We demonstrate the advantages of the molFET in terms of area, power, and speed by comparing it with a conventional MOSFET implementation. The results confirm the molecular technology is a promising candidate for accomplishing high neuron throughput capability and massive redundancy, still providing high energy efficiency. The obtained results foster further investigation of molFET technology both at the device and circuit level.

NanoSheet-Gate-All-Around-FETs (NS-GAAFETs) are commonly recognized as the future technology to push the digital node scaling into the sub-3 nm range. NS-GAAFETs are expected to replace FinFETs in a few years, as they provide highly electrostatic gate control thanks to the GAA structure, with four sides of the NS channel entirely enveloped by the gate. At the same time, the NS rectangular cross-section is demonstrated to be effective in its driving strength thanks to its high saturation current, tunable through the NS width used as a design parameter. In this work, we develop a NS-GAAFET compact model and we use it to link peculiar single-device parameters to digital circuit performance. In particular, we use the well-known BSIM-CMG core solver for multigate transistors as a starting point and develop an ad hoc resistive and capacitive network to model the NS-GAAFET geometrical and physical structure. Then, we employ the developed model to design and optimize a digital inverter and a five-stage ring oscillator, which we use as a performance benchmark for the NS-GAAFET technology. Through Cadence Virtuoso SPICE simulations, we investigate the digital NS-GAAFET performance for both high-performance and low-power nodes, according to the average future node present in the International Roadmap for Devices and Systems. We focus our analysis on the main different technological parameters with regard to FinFET, i.e., the inner and outer spacers. Our results highlight that in future technological nodes, the choice of alternative low-K dielectric materials for the NS spacers will assume increasing importance, being as relevant, or even more relevant, than photolithographic alignment and resolution at the sub-nm scale.

Food contamination by aflatoxins is an urgent global issue due to its high level of toxicity and the difficulties in limiting the diffusion. Unfortunately, current detection techniques, which mainly use biosensing, prevent the pervasive monitoring of aflatoxins throughout the agri-food chain. In this work, we investigate, through ab initio atomistic calculations, a pyrrole-based Molecular Field Effect Transistor (MolFET) as a single-molecule sensor for the amperometric detection of aflatoxins. In particular, we theoretically explain the gate-tuned current modulation from a chemical–physical perspective, and we support our insights through simulations. In addition, this work demonstrates that, for the case under consideration, the use of a suitable gate voltage permits a considerable enhancement in the sensor performance. The gating effect raises the current modulation due to aflatoxin from 100% to more than ${10}^3÷{10}^4$%. In particular, the current is diminished by two orders of magnitude from the μA range to the nA range due to the presence of aflatoxin B1. Our work motivates future research efforts in miniaturized FET electrical detection for future pervasive electrical measurement of aflatoxins.

We investigate through simulations the gold-C ₆₀ -gold molecular junction as a novel single-molecule amperometric gas sensor. We find it promising for NO and NO ₂ detection in air andat room temperature, with current variations of the order of the microampere, and presenting the potential capability of achieving the single-molecule sensitivity along with selectivity in presence of common atmospheric gases. Furthermore, and for the first time, we investigate the current modulation mechanism due to target-sensor intermolecular interactions, providing a theoretical insight in the functioning and exclusive properties of this novel device. In particular, we show and motivate the peculiar voltage-dependent response of the sensor, that we relate to the distinctive mechanism of transport modulation occurring in presence of a specific target. Finally, we discuss sensing reliability in air and the effects of probable fabrication process variability on the sensing performance. Our results motivate future works on molecular dot-based chemical sensors in terms of sensor-target exclusive interactions and detection principle, oriented to device-level engineering to find optimal operating conditions.

Molecular Field-Coupled Nanocomputing (MolFCN) represents a revolutionary approach to computational technology, exploiting single molecules for encoding and processing logical information. MolFCN permits zero-current logical operations to achieve ultra-low power and hyper-miniaturized computing units. This perspective article explores the current state and future potential of MolFCN, highlighting recent technological advancements, potential applications, and the significant challenges that lie ahead. Despite the challenges, the pathway to practical implementation holds significant promise, with obstacles such as scalability, stability, integration, and practical considerations offering opportunities for innovation and advancement. MolFCN can shape the future of nanocomputing and contribute to current major challenges in nanoelectronics by opening key research directions.