A Behavior-Learned Cross-Reactive Sensor Matrix for Intelligent Skin Perception.

Mimicking human skin sensation such as spontaneous multimodal perception and identification/discrimination of intermixed stimuli is severely hindered by the difficulty of efficient integration of complex cutaneous receptor-emulating circuitry and the lack of an appropriate protocol to discern the intermixed signals. Here, a highly stretchable cross-reactive sensor matrix is demonstrated, which can detect, classify, and discriminate various intermixed tactile and thermal stimuli using a machine-learning approach. Particularly, the multimodal perception ability is achieved by utilizing a learning algorithm based on the bag-of-words (BoW) model, where, by learning and recognizing the stimulus-dependent 2D output image patterns, the discrimination of each stimulus in various multimodal stimuli environments is possible. In addition, the single sensor device integrated in the cross-reactive sensor matrix exhibits multimodal detection of strain, flexion, pressure, and temperature. It is hoped that his proof-of-concept device with machine-learning-based approach will provide a versatile route to simplify the electronic skin systems with reduced architecture complexity and adaptability to various environments beyond the limitation of conventional "lock and key" approaches.

Ultrasensitive Electrical Detection of Hemagglutinin for Point-of-Care Detection of Influenza Virus Based on a CMP-NANA Probe and Top-Down Processed Silicon Nanowire Field-Effect Transistors.

Rather than the internal genome nucleic acids, the biomolecules on the surface of the influenza virus itself should be detected for a more exact and rapid point-of-care yes/no decision for influenza virus-induced infectious diseases. This work demonstrates the ultrasensitive electrical detection of the HA1 domain of hemagglutinin (HA), a representative viral surface protein of the influenza virus, using the top-down complementary metal oxide semiconductor (CMOS) processed silicon nanowire (SiNW) field-effect transistor (FET) configuration. Cytidine-5'-monophospho-N-acetylneuraminic acid (CMP-NANA) was employed as a probe that specifically binds both to the aldehyde self-aligned monolayer on the SiNWs and to HA1 simultaneously. CMP-NANA was serially combined with two kinds of linkers, namely 3-aminopropyltriethoxysilane and glutaraldehyde. The surface functionalization used was verified using the purification of glutathione S-transferase-tagged HA1, contact angle measurement, enzyme-linked immunosorbent assay test, and isoelectric focusing analysis. The proposed functionalized SiNW FET showed high sensitivities of the threshold voltage shift (ΔVT) ~51 mV/pH and the ΔVT = 112 mV (63 mV/decade) with an ultralow detectable range of 1 fM of target protein HA1.

Time-Shared Twin Memristor Crossbar Reducing the Number of Arrays by Half for Pattern Recognition.

In this paper, we propose a new time-shared twin memristor crossbar for pattern-recognition applications. By sharing two memristor arrays at different time, the number of memristor arrays can be reduced by half, saving the crossbar area by half, too. To implement the time-shared twin memristor crossbar, we also propose CMOS time-shared subtractor circuit, in this paper. The operation of the time-shared twin memristor crossbar is verified using 3 × 3 memristor array which is made of aluminum film and carbon fiber. Here, the crossbar array is programmed to store three different patterns. When we apply three different input vectors to the array, we can verify that the input vectors are well recognized by the proposed crossbar. Moreover, the proposed crossbar is tested for the recognition of complicated gray-scale images. Here, 10 images with 32 × 32 pixels are applied to the proposed crossbar. The simulation result verifies that the input images are recognized well by the proposed crossbar, even though the noise level of each image is varied from -10 to +10 dB.

The Analysis of Characteristics in Dry and Wet Environments of Silicon Nanowire-Biosensor.

Our study investigates differences in sensitivity of dry and wet environment in the field of biosensing experiment in detail and depth. The sensitivity of biosensing varies by means of surrounding conditions of silicon nanowire field effect transistor (SiNW FET). By examining charged polymer reaction in the silicon nanowire transistor (SiNW), we have discovered that the threshold voltage (V(T)) shift and change of subthreshold slope (SS) in wet environment are smaller than that of the air. Furthermore, we analyzed the sensitivity through modifying electrolyte concentration in the wet condition, and confirmed that V(T) shift increases in low concentration condition of phosphate buffered saline (PBS) due to the Debye length. We believe that the results we have found in this study would be the cornerstone in contributing to advanced biosensing experiment in the future.

Comparative Study on Statistical-Variation Tolerance Between Complementary Crossbar and Twin Crossbar of Binary Nano-scale Memristors for Pattern Recognition.

This paper performs a comparative study on the statistical-variation tolerance between two crossbar architectures which are the complementary and twin architectures. In this comparative study, 10 greyscale images and 26 black-and-white alphabet characters are tested using the circuit simulator to compare the recognition rate with varying statistical variation and correlation parameters.As with the simulation results of 10 greyscale image recognitions, the twin crossbar shows better recognition rate by 4 % on average than the complementary one, when the inter-array correlation = 1 and intra-array correlation = 0. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture can recognize better by 5.6 % on average than the complementary one.Similarly, when the inter-array correlation = 1 and intra-array correlation = 0, the twin architecture can recognize 26 alphabet characters better by 4.5 % on average than the complementary one. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture is better by 6 % on average than the complementary one. By summary, we can conclude that the twin crossbar is more robust than the complementary one under the same amounts of statistical variation and correlation.