Simulation of a randomly percolated CNT network for an improved analog physical unclonable function.

Carbon nanotube networks (CNTs)-based devices are well suited for the physically unclonable function (PUF) due to the inherent randomness of the CNT network, but CNT networks can vary significantly during manufacturing due to various controllable process conditions, which have a significant impact on PUF performance. Therefore, optimization of process conditions is essential to have a PUF with excellent performance. However, because it is time-consuming and costly to fabricate directly under various conditions, we implement randomly formed CNT network using simulation and confirm the variable correlation of the CNT network optimized for PUF performance. At the same time, by implementing an analog PUF through simulation, we present a 2D patterned PUF that has excellent security and can compensate for error occurrence problems. To evaluate the performance of analog PUF, a new evaluation method different from the existing digital PUF is proposed, and the PUF performance is compared according to two process variables, CNT density and metallic CNT ratio, and the correlation with PUF performance is confirmed. This study can serve as a basis for research to produce optimized CNT PUF by applying simulation according to the needs of the process of forming a CNT network.

Micrometer Scale Resolution Limit of a Fiber-Coupled Electro-Optic Probe.

We present the practical resolution limit of a fine electrical structure based on a fiber-coupled electro-optic probing system. The spatial resolution limit was experimentally evaluated on the sub-millimeter to micrometer scale of planar electrical transmission lines. The electrical lines were fabricated to have various potential differences depending on the dimensions and geometry. The electric field between the lines was measured through an electro-optic probe, which was miniaturized up to the optical bare fiber scale so as to investigate the spatial limit of electrical signals with minimal invasiveness. The experimental results show that the technical resolution limitation of a fiber-coupled probe can reasonably approach a fraction of the mode field diameter (~10 µm) of the fiber in use.

Synaptic Device Network Architecture with Feature Extraction for Unsupervised Image Classification.

For the efficient recognition and classification of numerous images, neuroinspired deep learning algorithms have demonstrated their substantial performance. Nevertheless, current deep learning algorithms that are performed on von Neumann machines face significant limitations due to their inherent inefficient energy consumption. Thus, alternative approaches (i.e., neuromorphic systems) are expected to provide more energy-efficient computing units. However, the implementation of the neuromorphic system is still challenging due to the uncertain impacts of synaptic device specifications on system performance. Moreover, only few studies are reported how to implement feature extraction algorithms on the neuromorphic system. Here, a synaptic device network architecture with a feature extraction algorithm inspired by the convolutional neural network is demonstrated. Its pattern recognition efficacy is validated using a device-to-system level simulation. The network can classify handwritten digits at up to a 90% recognition rate despite using fewer synaptic devices than the architecture without feature extraction.

Three-Dimensional Printed Poly(vinyl alcohol) Substrate with Controlled On-Demand Degradation for Transient Electronics.

Electronics that degrade after stable operation for a desired operating time, called transient electronics, are of great interest in many fields, including biomedical implants, secure memory devices, and environmental sensors. Thus, the development of transient materials is critical for the advancement of transient electronics and their applications. However, previous reports have mostly relied on achieving transience in aqueous solutions, where the transience time is largely predetermined based on the materials initially selected at the beginning of the fabrication. Therefore, accurate control of the transience time is difficult, thereby limiting their application. In this work, we demonstrate transient electronics based on a water-soluble poly(vinyl alcohol) (PVA) substrate on which carbon nanotube (CNT)-based field-effect transistors were fabricated. We regulated the structural parameters of the PVA substrate using a three-dimensional (3D) printer to accurately control and program the transience time of the PVA substrate in water. The 3D printing technology can produce complex objects directly, thus enabling the efficient fabrication of a transient substrate with a prescribed and controlled transience time. In addition, the 3D printer was used to develop a facile method for the selective and partial destruction of electronics.

Three-Dimensionally Printed Micro-electromechanical Switches.

Three-dimensional (3D) printers have attracted considerable attention from both industry and academia and especially in recent years because of their ability to overcome the limitations of two-dimensional (2D) processes and to enable large-scale facile integration techniques. With 3D printing technologies, complex structures can be created using only a computer-aided design file as a reference; consequently, complex shapes can be manufactured in a single step with little dependence on manufacturer technologies. In this work, we provide a first demonstration of the facile and time-saving 3D printing of two-terminal micro-electromechanical (MEM) switches. Two widely used thermoplastic materials were used to form 3D-printed MEM switches; freely suspended and fixed electrodes were printed from conductive polylactic acid, and a water-soluble sacrificial layer for air-gap formation was printed from poly(vinyl alcohol). Our 3D-printed MEM switches exhibit excellent electromechanical properties, with abrupt switching characteristics and an excellent on/off current ratio value exceeding 106. Therefore, we believe that our study makes an innovative contribution with implications for the development of a broader range of 3D printer applications (e.g., the manufacturing of various MEM devices and sensors), and the work highlights a uniquely attractive path toward the realization of 3D-printed electronics.

Impact of Synaptic Device Variations on Pattern Recognition Accuracy in a Hardware Neural Network.

Neuromorphic systems (hardware neural networks) derive inspiration from biological neural systems and are expected to be a computing breakthrough beyond conventional von Neumann architecture. Interestingly, in neuromorphic systems, the processing and storing of information can be performed simultaneously by modulating the connection strength of a synaptic device (i.e., synaptic weight). Previously investigated synaptic devices can emulate the functionality of biological synapses successfully by utilizing various nano-electronic phenomena; however, the impact of intrinsic synaptic device variability on the system performance has not yet been studied. Here, we perform a device-to-system level simulation of different synaptic device variation parameters in a designed neuromorphic system that has the potential for unsupervised learning and pattern recognition. The effects of variations in parameters such as the weight modulation nonlinearity (NL), the minimum-maximum weight (G min and G max ), and the weight update margin (ΔG) on the pattern recognition accuracy are analyzed quantitatively. These simulation results can provide guidelines for the continued design and optimization of a synaptic device for realizing a functional large-scale neuromorphic computing system.

Determination of individual contact interfaces in carbon nanotube network-based transistors.

Carbon nanotubes (CNTs) used as semiconducting channels induce high mobility, thermal conductivity, mechanical flexibility, and chemical stability in field-effect, thin-film transistors (TFTs). However, the contact interfaces in CNT-TFTs have contact resistances that are difficult to reduce; this contact resistance can eventually limit the overall performance of CNT-TFTs. The contact interface between the source/drain electrodes and CNTs, especially for those CNT-TFTs in which the channel comprises randomly networked CNTs, plays a particularly dominant role in determining the performance and degree of variability in CNT-TFTs. However, no studies have reported a determination method that individually extracts each contact resistance at the source/drain electrodes. The present work presents an efficient method for directly determining the contact interfaces in CNT-TFTs by extracting each contact resistance produced at the source (R S ) and drain (R D ) electrodes. Moreover, we comprehensively simulated the randomly networked CNTs using an in-depth Monte-Carlo method, which provides an efficient method for visualizing the uniformity of a CNT network with various controllable CNT parameters. The proposed method provides guidance and a means for optimizing the design of the CNT network channel in CNT-TFTs and additional insights into improving the performance of CNT-TFTs.

Transparent, Flexible Strain Sensor Based on a Solution-Processed Carbon Nanotube Network.

The demands for transparent, flexible electronic devices are continuously increasing due to their potential applications to the human body. In particular, skin-like, transparent, flexible strain sensors have been developed to realize multifunctional human-machine interfaces. Here, we report a sandwich-like structured strain sensor with excellent optical transparency based on highly purified, solution-processed, 99% metallic CNT-polydimethylsiloxane (PDMS) composite thin films. Our CNT-PDMS composite strain sensors are mechanically compliant, physically robust, and easily fabricated. The fabricated strain sensors exhibit a high optical transparency of over 92% in the visible range with acceptable sensing performances in terms of sensitivity, hysteresis, linearity, and drift. We also found that the sensitivity and linearity of the strain sensors can be controlled by the number of CNT sprays; hence, our sensor can be applied and controlled based on the need of individual applications. Finally, we investigated the detections of human activities and emotions by mounting our transparent strain sensor on various spots of human skins.

Pattern Recognition Using Carbon Nanotube Synaptic Transistors with an Adjustable Weight Update Protocol.

Recent electronic applications require an efficient computing system that can perform data processing with limited energy consumption. Inspired by the massive parallelism of the human brain, a neuromorphic system (hardware neural network) may provide an efficient computing unit to perform such tasks as classification and recognition. However, the implementation of synaptic devices (i.e., the essential building blocks for emulating the functions of biological synapses) remains challenging due to their uncontrollable weight update protocol and corresponding uncertain effects on the operation of the system, which can lead to a bottleneck in the continuous design and optimization. Here, we demonstrate a synaptic transistor based on highly purified, preseparated 99% semiconducting carbon nanotubes, which can provide adjustable weight update linearity and variation margin. The pattern recognition efficacy is validated using a device-to-system level simulation framework. The enlarged margin rather than the linear weight update can enhance the fault tolerance of the recognition system, which improves the recognition accuracy.

Three-Dimensional Fin-Structured Semiconducting Carbon Nanotube Network Transistor.

Three-dimensional (3-D) fin-structured carbon nanotube field-effect transistors (CNT-FETs) with purified 99.9% semiconducting CNTs were demonstrated on a large scale 8 in. silicon wafer. The fabricated 3-D CNT-FETs take advantage of the 3-D geometry and exhibit enhanced electrostatic gate controllability and superior charge transport. A trigated structure surrounding the randomly networked single-walled CNT channel was formed on a fin-like 3-D silicon frame, and as a result, the effective packing density increased to almost 600 CNTs/µm. Additionally, highly sensitive controllability of the threshold voltage (VTH) was achieved using a thin back gate oxide in the same silicon frame to control power consumption and enhance performance. Our results are expected to broaden the design margin of CNT-based circuit architectures for versatile applications. The proposed 3-D CNT-FETs can potentially provide a desirable alternative to silicon based nanoelectronics and a blueprint for furthering the practical use of emerging low-dimensional materials other than CNTs.

Evaluating liquid crystal properties for use in terahertz devices.

Despite the wide application of liquid crystals (LCs) in the visible frequency range, their properties in the terahertz range have not yet been extensively investigated. In this paper we have investigated the terahertz properties of LCs E7, BL037, RDP-94990 and RDP-97304 using terahertz time-domain-spectroscopy. We find that RDP-94990 has the largest birefringence and smallest absorption in the terahertz range compared to E7 and BL037. We highlight the importance of investigating all parameters, not just the birefringence, when designing fast, efficient and transmissive terahertz LC devices.

Quantitative analysis of water distribution in human articular cartilage using terahertz time-domain spectroscopy.

The water distribution in human osteoarthritic articular cartilage has been quantitatively characterized using terahertz time-domain spectroscopy (THz TDS). We measured the refractive index and absorption coefficient of cartilage tissue in the THz frequency range. Based on our measurements, the estimated water content was observed to decrease with increasing depth cartilage tissue, showing good agreement with a previous report based on destructive biochemical methods.

Quantitative analysis and measurements of near-field interactions in terahertz microscopes.

We demonstrated quantitative analysis and measurements of near-fields interactions in a terahertz pulse near-field microscope. We developed a self-consistent line dipole image method for the quantitative analysis of the near-field interaction in THz scattering-type scanning optical microscopes. The measurements of approach curves and relative contrasts on gold and silicon substrates were in excellent agreement with calculations.