Systems for Implementing Data Communication with Security Tokens

This paper proposes a novel scheme for inter-connecting IOT devices with servers. To overcome the drawbacks and other shortcomings of existing IoT network schemes, a new approach to IoT device certification and inter-connecting IoT devices to other network devices (e.g., aggregators and servers) is described. The proposed approach ensures that the overall IoT network is “hardened” against attack and meets the stringent requirements of mission critical applications.

Quantum Dot Gate (QDG) Quantum Dot Channel (QDC) Multistate Logic Non-Volatile Memory (NVM) with High-K Dielectric HfO2 Barriers

This paper presents the theory, fabrication and experimental testing results for a multiple state Non-Volatile Memory (NVM), comprised of hafnium oxide high-k dielectric tunnel and gate barriers as well as a Silicon Quantum Dot Superlattice (QDSL) implemented for the floating gate and inversion channel (QDG) and (QDC) respectively. With the conclusion of Moore’s Law for conventional transistor fabrication, regarding the minimum gate size, current efforts in memory cell research and development are focused on bridging the gap between the conventions of the past sixty years and the future of computing. One method of continuing the increasing chip density is to create multistate devices capable of storing and processing additional logic states beyond 1 and 0. Replacing the silicon nitride floating gate of a conventional Flash NVM with QDSL gives rise to minibands that result in greater control over charge levels stored in the QDG and additional intermediate states. Utilizing Hot Carrier Injection (HCI) programming, for the realized device, various magnitudes of gate voltage pulses demonstrated the ability to accurately control the charge levels stored in the QDG. This corresponds to multiple threshold voltage shifts allowing detection of multiple states during read operations.

Quantum-Dot Transistor Based Multi-Bit Multiplier Unit for In-Memory Computing

In-memory computing is an emerging technique to fulfill the fast growing demand for high-performance data processing. This technique provides fast processing and high throughput by accessing data stored in the memory array rather than dealing with complicated operation and data movement on hard drive. For data processing, the most important computation is dot product, which is also the core computation for applications such as deep learning neuron networks, machine learning, etc. As multiplication is the key function in dot product, it is critical to improve its performance and achieve faster memory processing. In this paper, we present a design with the ability to perform in-memory multi-bit multiplications. The proposed design is implemented by using quantum-dot transistors, which enable multi-bit computations in the memory cell. Experimental results demonstrate that the proposed design provides reliable in-memory multi-bit multiplications with high density and high energy efficiency. Statistical analysis is performed using Monte Carlo simulations to investigate the process variations and error effects.

A Novel Peripheral Circuit for SWSFET Based Multivalued Static Random-Access Memory

This paper presents the peripheral circuitry for a multivalued static random-access memory (SRAM) based on 2-bit CMOS cross-coupled inverters using spatial wavefunction switched (SWS) field effect transistors (SWSFETs). The novel feature is a two quantum well/quantum dot channel n-SWSFET access transistor. The reduction in area with four-bit storage-per-cell increases the memory density and efficiency of the SRAM array. The SWSFET has vertically stacked two-quantum well/quantum dot channels between the source and drain regions. The upper or lower quantum charge locations in the channel region is based on the input gate voltage. The analog behavioral modeling (ABM) of the SWSFET device is done using conventional BSIM 3V3 device parameters in 90 nm technology. The Cadence circuit simulations for the proposed memory cell and addressing/peripheral circuitry are presented.

Amaranthine: Humanoid Robot Kinematics

Humanoid robots are employed in a wide range of fields to replicate human actions. This paper presents the mechanism, configuration, mathematical modeling, and workspace of a 3D printed humanoid robot – Amaranthine. It also discusses the potential scope of humanoid robots in the present day and future. Robots can be programmed for automation as per the demand of the task or operations to be performed. Humanoid robots, while being one of the small groups of service robots in the current market, have the greatest potential to become the industrial tool of the future. Introducing a Humanoid Robot-like Amaranthine holds huge scope majorly in the fields of medical assistance, teaching aid, large industries where heavy-duty operations require application-specific software, etc. Amaranthine was 3D printed and assembled at the RISC Lab of University of Bridgeport.

Modeling of Quantum Dot Channel (QDC) Si FETs at Sub-Kelvin for Multi-State Logic

Multi-state room temperature operation of SiOx-cladded Si quantum dots (QD) and GeOx-cladded Ge quantum dot channel (QDC) field-effect transistors (FETs) and spatial wavefunction switched (SWS)-FETs have been experimentally demonstrated. This paper presents simulation of cladded Si and Ge quantum dot channel (QDC) field-effect transistors at 4.2°K and milli-Kelvin temperatures. An array of thin oxide barrier/cladding (∼1nm) on quantum dots forms a quantum dot superlattice (QDSL). A gradual channel approximation model using potential and inversion layer charge density nQM, obtained by the self-consistent solution of the Schrodinger and Poisson’s equations, is shown to predict I-V characteristics up to milli-Kelvin temperatures. Physics-based equivalent circuit models do not work below 53°K. However, they may be improved by adapting parameters derived from quantum simulations. Low-temperature operation improves noise margins in QDC- and SWS-FET based multi-bit logic, which dissipates lower power and comprise of fewer device count. In addition, the role of self-assembled cladded QDs with transfer gate provides a novel pathway to implement qubit processing.

Comparison of Buffer Layer Grading Approaches in InGaAs/GaAs (001)

Metamorphic semiconductor devices often utilize compositionally-graded buffer layers for the accommodation of the lattice mismatch with controlled threading dislocation density and residual strain. Linear or step-graded buffers have been used extensively in these applications, but there are indications that sublinear, superlinear, S-graded, or overshoot graded structures could offer advantages in the control of defect densities. In this work we compare linear, step-graded, and nonlinear grading approaches in terms of the resulting strain and dislocations density profiles using a state-of-the-art model for strain relaxation and dislocation dynamics. We find that sublinear grading results in lower surface dislocation densities than either linear or superlinear grading approaches.

Design of a Smart Maximum Power Point Tracker (MPPT) for RF Energy Harvester

Continuous enhancement of the performance of energy harvesters in recent years has broadened their arenas of applications. On the other hand, ample availability of IoT devices has made radio frequency (RF) a viable source of energy harvesting. Integration of a maximum power point tracking (MPPT) controller in RF energy harvester is a necessity that ensures maximum available power transfer with variable input power conditions. In this paper, FPGA implementation of a machine learning (ML) model for maximum power point tracking in RF energy harvesters is presented. A supervised learning-based ML model-feedforward neural network (FNN) has been designed which is capable of tracking maximum power point with optimal accuracy. The model was designed using stochastic gradient descent (SGD) optimizer and mean square error (MSE) loss function. Simulation results of the VHDL translated model demonstrated a good agreement between the expected and the obtained values. The proposed ML based MPPT controller was implemented in Artix-7 Field Programmable Gate Array (FPGA).