Refine
Document Type
- Article (reviewed) (19)
- Conference Proceeding (9)
- Part of a Book (1)
- Article (unreviewed) (1)
Conference Type
- Konferenzartikel (9)
Language
- English (30)
Has Fulltext
- no (30)
Is part of the Bibliography
- yes (30)
Keywords
- printed electronics (4)
- oxide electronics (2)
- Electrolyte-gated transistors (1)
- Feldeffekt (1)
- Halbleiter (1)
- Printed Electronics (1)
- Transistortechnologie (1)
- copper oxide (1)
- crossbar (1)
- diode modeling (1)
- electrolyte-gated transistors (1)
- fingerprinting (1)
- hybrid systems (1)
- identification (1)
- indium oxide (1)
- metal oxide transistor (1)
- nanotechnology (1)
- neural networks (1)
- nickel oxide (1)
- oxide semiconductors (1)
- performance of ring oscillators (1)
- physically unclonable function (PUF) (1)
- pn-diode (1)
- stochastic computing (1)
- transistor model (1)
Institute
Open Access
- Closed Access (30) (remove)
Novel manufacturing technologies, such as printed electronics, may enable future applications for the Internet of Everything like large-area sensor devices, disposable security, and identification tags. Printed physically unclonable functions (PUFs) are promising candidates to be embedded as hardware security keys into lightweight identification devices. We investigate hybrid PUFs based on a printed PUF core. The statistics on the intra- and inter-hamming distance distributions indicate a performance suitable for identification purposes. Our evaluations are based on statistical simulations of the PUF core circuit and the thereof generated challenge-response pairs. The analysis shows that hardware-intrinsic security features can be realized with printed lightweight devices.
Uncontrollable manufacturing variations in electrical hardware circuits can be exploited as Physical Unclonable Functions (PUFs). Herein, we present a Printed Electronics (PE)-based PUF system architecture. Our proposed Differential Circuit PUF (DiffC-PUF) is a hybrid system, combining silicon-based and PE-based electronic circuits. The novel approach of the DiffC-PUF architecture is to provide a specially designed real hardware system architecture, that enables the automatic readout of interchangeable printed DiffC-PUF core circuits. The silicon-based addressing and evaluation circuit supplies and controls the printed PUF core and ensures seamless integration into silicon-based smart systems. Major objectives of our work are interconnected applications for the Internet of Things (IoT).
Neuromorphic computing systems have demonstrated many advantages for popular classification problems with significantly less computational resources. We present in this paper the design, fabrication and training of a programmable neuromorphic circuit, which is based on printed electrolytegated field-effect transistor (EGFET). Based on printable neuron architecture involving several resistors and one transistor, the proposed circuit can realize multiply-add and activation functions. The functionality of the circuit, i.e. the weights of the neural network, can be set during a post-fabrication step in form of printing resistors to the crossbar. Besides the fabrication of a programmable neuron, we also provide a learning algorithm, tailored to the requirements of the technology and the proposed programmable neuron design, which is verified through simulations. The proposed neuromorphic circuit operates at 5V and occupies 385mm 2 of area.
In many application domains, in particular automotives, guaranteeing a very low failure rate is crucial to meet functional and safety standards. Especially, reliable operation of memory components such as SRAM cells is of essential importance. Due to aggressive technology downscaling, process and runtime variations significantly impact manufacturing yield as well as functionality. For this reason, a thorough memory failure rate assessment is imperative for correct circuit operation and yield improvement. In this regard, Monte Carlo simulations have been used as the conventional method to estimate the variability induced failure rate of memory components. However, Monte Carlo methods become infeasible when estimating rare events such as high-sigma failure rates. To this end, Importance Sampling methods have been proposed which reduce the number of required simulations substantially. However, existing methods still suffer from inaccuracies and high computational efforts, in particular for high-sigma problems. In this paper, we fill this gap by presenting an efficient mixture Importance Sampling approach based on Bayesian optimization, which deploys a surface model of the objective function to find the most probable failure points. Its advantages include constant complexity independent of the dimensions of design space, the potential to find the global extrema, and higher trustworthiness of the estimated failure rate by accurately exploring the design space. The approach is evaluated on a 6T-SRAM cell as well as a master-slave latch based on a 28nm FDSOI process. The results show an improvement in accuracy, resulting in up to 63× better accuracy in estimating failure rates compared to the best state-of-the-art solutions on a 28nm technology node.
Printed electronics (PE) offers flexible, extremely low-cost, and on-demand hardware due to its additive manufacturing process, enabling emerging ultra-low-cost applications, including machine learning applications. However, large feature sizes in PE limit the complexity of a machine learning classifier (e.g., a neural network (NN)) in PE. Stochastic computing Neural Networks (SC-NNs) can reduce area in silicon technologies, but still require complex designs due to unique implementation tradeoffs in PE. In this paper, we propose a printed mixed-signal system, which substitutes complex and power-hungry conventional stochastic computing (SC) components by printed analog designs. The printed mixed-signal SC consumes only 35% of power consumption and requires only 25% of area compared to a conventional 4-bit NN implementation. We also show that the proposed mixed-signal SC-NN provides good accuracy for popular neural network classification problems. We consider this work as an important step towards the realization of printed SC-NN hardware for near-sensor-processing.
Morphological transition of a rod-shaped phase into a string of spherical particles is commonly observed in the microstructures of alloys during solidification (Ratke and Mueller, 2006). This transition phenomenon can be explained by the classic Plateau-Rayleigh theory which was derived for fluid jets based on the surface area minimization principle. The quintessential work of Plateau-Rayleigh considers tiny perturbations (amplitude much less than the radius) to the continuous phase and for large amplitude perturbations, the breakup condition for the rod-shaped phase is still a knotty issue. Here, we present a concise thermodynamic model based on the surface area minimization principle as well as a non-linear stability analysis to generalize Plateau-Rayleigh’s criterion for finite amplitude perturbations. Our results demonstrate a breakup transition from a continuous phase via dispersed particles towards a uniform-radius cylinder, which has not been found previously, but is observed in our phase-field simulations. This new observation is attributed to a geometric constraint, which was overlooked in former studies. We anticipate that our results can provide further insights on microstructures with spherical particles and cylinder-shaped phases.
Amorphous In-Ga-Zn-O (IGZO) is a high-mobility semiconductor employed in modern thin-film transistors for displays and it is considered as a promising material for Schottky diode-based rectifiers. Properties of the electronic components based on IGZO strongly depend on the manufacturing parameters such as the oxygen partial pressure during IGZO sputtering and post-deposition thermal annealing. In this study, we investigate the combined effect of sputtering conditions of amorphous IGZO (In:Ga:Zn=1:1:1) and post-deposition thermal annealing on the properties of vertical thin-film Pt-IGZO-Cu Schottky diodes, and evaluated the applicability of the fabricated Schottky diodes for low-frequency half-wave rectifier circuits. The change of the oxygen content in the gas mixture from 1.64% to 6.25%, and post-deposition annealing is shown to increase the current rectification ratio from 10 5 to 10 7 at ±1 V, Schottky barrier height from 0.64 eV to 0.75 eV, and the ideality factor from 1.11 to 1.39. Half-wave rectifier circuits based on the fabricated Schottky diodes were simulated using parameters extracted from measured current-voltage and capacitance-voltage characteristics. The half-wave rectifier circuits were realized at 100 kHz and 300 kHz on as-fabricated Schottky diodes with active area of 200 μm × 200 μm, which is relevant for the near-field communication (125 kHz - 134 kHz), and provided the output voltage amplitude of 0.87 V for 2 V supply voltage. The simulation results matched with the measurement data, verifying the model accuracy for circuit level simulation.
Development of Fully Printed Oxide Field-Effect Transistors using Graphene Passive Structures
(2019)
During the past decade to the present time, the topic of printed electronics has gained a lot of attention for their potential use in a number of practical applications, including biosensors, photovoltaic devices, RFIDs, flexible displays, large-area circuits, and so on. To fully realize printed electronic components and devices, effective techniques for the printing of passive structures and electrically and chemically compatible materials in the printed devices need to be developed first. The opportunity of using electrically conducting graphene inks will enable the integration of passive structures into active devices, as for example, printed electrolyte-gated transistors (EGTs). Accordingly, in this study, we present the parametric results obtained on fully printed electrolyte-gated transistors having graphene as the passive electrodes, an inorganic oxide semiconductor as the active channel, and a composite solid polymer electrolyte (CSPE) as the gate insulating material. This configuration offers high chemical and electrical stability while at the same time allowing EGT operation at low potentials, implying the distinct advantage of operation at low input voltages. The printed in-plane EGTs we developed exhibit excellent performance with device mobility up to 16 cm2 V–1 s–1, an ION/IOFF ratio of 105, and a subthreshold slope of 120 mV dec–1.
Physically Unclonable Functions (PUFs) are hardware-based security primitives, which allow for inherent device fingerprinting. Therefore, intrinsic variation of imperfect manufactured systems is exploited to generate device-specific, unique identifiers. With printed electronics (PE) joining the internet of things (IoT), hardware-based security for novel PE-based systems is of increasing importance. Furthermore, PE offers the possibility for split-manufacturing, which mitigates the risk of PUF response readout by third parties, before commissioning. In this paper, we investigate a printed PUF core as intrinsic variation source for the generation of unique identifiers from a crossbar architecture. The printed crossbar PUF is verified by simulation of a 8×8-cells crossbar, which can be utilized to generate 32-bit wide identifiers. Further focus is on limiting factors regarding printed devices, such as increased parasitics, due to novel materials and required control logic specifications. The simulation results highlight, that the printed crossbar PUF is capable to generate close-to-ideal unique identifiers at the investigated feature size. As proof of concept a 2×2-cells printed crossbar PUF core is fabricated and electrically characterized.
Printed Electronics technology is a key-enabler for smart sensors, soft robotics, and wearables. The inkjet printed electrolyte-gated field effect transistor (EGFET) technology is a promising candidate for such applications due to its low-power operation, high field-effect mobility, and on-demand fabrication. Unlike conventional silicon-based technologies, inkjet printed electronics technology is an additive manufacturing process where multiple layers are printed on top of each other to realize functional devices such as transistors and their interconnections. Due to the additive manufacturing process, the technology has limited routing layers. For routing of complex circuits, insulating crossovers are printed at the intersection of routing paths to isolate them. The crossover can alter the electrical properties of a circuit based on specific location on a routing path. In this work, we propose a crossover-aware placement and routing (COPnR) methodology for inkjet-printed circuits by integrating the crossover constraints in our design framework. Our proposed placement methodology is based on a state-of-the-art evolutionary algorithm while the routing optimization is done using a genetic algorithm. The proposed methodology is compared with the industrial standard placement and routing (PnR) tools. On average, the proposed methodology has 38% fewer crossovers and 94% fewer failing paths compared to the industrial PnR tools applied to printed circuit designs.