One-Shot Remote Integration of Macromolecular Synaptic Elements on a Chip for Ultrathin Flexible Neural Network System.

The field of biomimetic electronics that mimic synaptic functions has expanded significantly to overcome the limitations of the von Neumann bottleneck. However, the scaling down of the technology has led to an increasingly intricate manufacturing process. To address the issue, this work presents a one-shot integrable electropolymerization (OSIEP) method with remote controllability for the deposition of synaptic elements on a chip by exploiting bipolar electrochemistry. Condensing synthesis, deposition, and patterning into a single fabrication step is achieved by combining alternating-current voltage superimposed on direct-current voltage-bipolar electropolymerization and a specially designed dual source/drain bipolar electrodes. As a result, uniform 6 × 5 arrays of poly(3,4-ethylenedioxythiophene) channels are successfully fabricated on flexible ultrathin parylene substrates in one-shot process. The channels exhibited highly uniform characteristics and are directly used as electrochemical synaptic transistor with synaptic plasticity over 100 s. The synaptic transistors have demonstrated promising performance in an artificial neural network (NN) simulation, achieving a high recognition accuracy of 95.20%. Additionally, the array of synaptic transistor is easily reconfigured to a multi-gate synaptic circuit to implement the principles of operant conditioning. These results provide a compelling fabrication strategy for realizing cost-effective and disposable NN systems with high integration density.

Highly Reliable 3D Channel Memory and Its Application in a Neuromorphic Sensory System for Hand Gesture Recognition.

Brain-inspired neuromorphic computing systems, based on a crossbar array of two-terminal multilevel resistive random-access memory (RRAM), have attracted attention as promising technologies for processing large amounts of unstructured data. However, the low reliability and inferior conductance tunability of RRAM, caused by uncontrollable metal filament formation in the uneven switching medium, result in lower accuracy compared to the software neural network (SW-NN). In this work, we present a highly reliable CoOx-based multilevel RRAM with an optimized crystal size and density in the switching medium, providing a three-dimensional (3D) grain boundary (GB) network. This design enhances the reliability of the RRAM by improving the cycle-to-cycle endurance and device-to-device stability of the I-V characteristics with minimal variation. Furthermore, the designed 3D GB-channel RRAM (3D GB-RRAM) exhibits excellent conductance tunability, demonstrating high symmetricity (624), low nonlinearity (ßLTP/ßLTD ∼ 0.20/0.39), and a large dynamic range (Gmax/Gmin ∼ 31.1). The cyclic stability of long-term potentiation and depression also exceeds 100 cycles (105 voltage pulses), and the relative standard deviation of Gmax/Gmin is only 2.9%. Leveraging these superior reliability and performance attributes, we propose a neuromorphic sensory system for finger motion tracking and hand gesture recognition as a potential elemental technology for the metaverse. This system consists of a stretchable double-layered photoacoustic strain sensor and a crossbar array neural network. We perform training and recognition tasks on ultrasonic patterns associated with finger motion and hand gestures, attaining a recognition accuracy of 97.9% and 97.4%, comparable to that of SW-NN (99.8% and 98.7%).

Improved Oxidation Resistance of Graphite Block by Introducing Curing Process of Phenolic Resin.

The purpose of this study is to improve the oxidation resistance of graphite blocks after graphitization at 2800 °C by introducing a curing process of phenolic resin, used as a binder to control the pore size. Using the methylene index obtained from FTIR, the curing temperature was set to 150 °C, the temperature at which cross-linking most highly occurs. Graphite blocks that had undergone curing, and were carbonized with a slow heating rate, showed increased mechanical and electrical properties. Microstructural observation confirmed that the curing process inhibited the formation of large pores in the graphite block. Therefore, the cured graphite block showed better oxidation resistance in air than a non-cured graphite block. Oxidation of the graphite block was caused by pores created by pyrolysis of the phenolic resin binder, which acted as active sites.

Micro-ultrasonic Assessment of Early Stage Clot Formation and Whole Blood Coagulation Using an All-Optical Ultrasound Transducer and Adaptive Signal Processing Algorithm.

Abnormal formation of solid thrombus inside a blood vessel can cause thrombotic morbidity and mortality. This necessitates early stage diagnosis, which requires quantitative assessment with a small volume, for effective therapy with low risk to unwanted development of various diseases. We propose a micro-ultrasonic diagnosis using an all-optical ultrasound-based spectral sensing (AOUSS) technique for sensitive and quantitative characterization of early stage and whole blood coagulation. The AOUSS technique detects and analyzes minute viscoelastic variations of blood at a micro-ultrasonic spot (<100 µm) defined by laser-generated focused ultrasound (LGFU). This utilizes (1) a uniquely designed optical transducer configuration for frequency-spectral matching and wideband operation (6 dB widths: 7-32 MHz and d.c. ∼ 46 MHz, respectively) and (2) an empirical mode decomposition (EMD)-based signal process particularly adapted to nonstationary LGFU signals backscattered from the spot. An EMD-derived spectral analysis enables one to assess viscoelastic variations during the initiation of fibrin formation, which occurs at a very early stage of blood coagulation (1 min) with high sensitivity (frequency transition per storage modulus increment = 8.81 MHz/MPa). Our results exhibit strong agreement with those obtained by conventional rheometry (Pearson's R > 0.95), which are also confirmed by optical microscopy. The micro-ultrasonic and high-sensitivity detection of AOUSS poses a potential clinical significance, serving as a screening modality to diagnose early stage clot formation (e.g., as an indicator for hypercoagulation of blood) and stages of blood-to-clot transition to check a potential risk for development into thrombotic diseases.

Auto-Tiler: Variable-Dimension Autoencoder with Tiling for Compressing Intermediate Feature Space of Deep Neural Networks for Internet of Things.

Due to limited resources of the Internet of Things (IoT) edge devices, deep neural network (DNN) inference requires collaboration with cloud server platforms, where DNN inference is partitioned and offloaded to high-performance servers to reduce end-to-end latency. As data-intensive intermediate feature space at the partitioned layer should be transmitted to the servers, efficient compression of the feature space is imperative for high-throughput inference. However, the feature space at deeper layers has different characteristics than natural images, limiting the compression performance by conventional preprocessing and encoding techniques. To tackle this limitation, we introduce a new method for compressing DNN intermediate feature space using a specialized autoencoder, called auto-tiler. The proposed auto-tiler is designed to include the tiling process and provide multiple input/output dimensions to support various partitioned layers and compression ratios. The results show that auto-tiler achieves 18% to 67% higher percent point accuracy compared to the existing methods at the same bitrate while reducing the process latency by 73% to 81%. The dimension variability of an auto-tiler also reduces the storage overhead by 62% with negligible accuracy loss.