A Nanoscale Bistable Resistor for an Oscillatory Neural Network.

Coupled oscillators construct an oscillatory neural network (ONN) by mimicking the interactions among neurons in the human brain. This work demonstrates a fully CMOS-based oscillator consisting of a bistable resistor (biristor), which shares a structure identical with that of a metal-oxide-semiconductor field-effect transistor, except for the use of a gate electrode. The biristor-based oscillator (birillator) generates oscillating voltage signals in the form of spikes due to a single transistor latch phenomenon. When two birillators are connected with a coupling capacitor, they become synchronized with a phase difference of 180°. These coupled oscillation characteristics are experimentally investigated for an ONN. As practical applications of the ONN with coupled birillators, edge detection and vertex coloring are conducted by encoding information into phase differences between them. The proposed fully CMOS-based birillators are advantageous for low power consumption, high CMOS compatibility, and a compact footprint area.

Bio-Inspired Organic Synaptor with In Situ Ion-Doped Ultrathin Polyelectrolyte Containing Acetylcholine-Like Cation.

An ion-based synaptic transistor (synaptor) is designed to emulate a biological synapse using controlled ion movements. However, developing a solid-state electrolyte that can facilitate ion movement while achieving large-scale integration remains challenging. Here, a bio-inspired organic synaptor (BioSyn) with an in situ ion-doped polyelectrolyte (i-IDOPE) is demonstrated. At the molecular scale, a polyelectrolyte containing the tert-amine cation, inspired by the neurotransmitter acetylcholine is synthesized using initiated chemical vapor deposition (iCVD) with in situ doping, a one-step vapor-phase deposition used to fabricate solid-state electrolytes. This method results in an ultrathin, but highly uniform and conformal solid-state electrolyte layer compatible with large-scale integration, a form that is not previously attainable. At a synapse scale, synapse functionality is replicated, including short-term and long-term synaptic plasticity (STSP and LTSP), along with a transformation from STSP to LTSP regulated by pre-synaptic voltage spikes. On a system scale, a reflex in a peripheral nervous system is mimicked by mounting the BioSyns on various substrates such as rigid glass, flexible polyethylene naphthalate, and stretchable poly(styrene-ethylene-butylene-styrene) for a decentralized processing unit. Finally, a classification accuracy of 90.6% is achieved through semi-empirical simulations of MNIST pattern recognition, incorporating the measured LTSP characteristics from the BioSyns.

Cryptographic triboelectric random number generator with gentle breezes of an entropy source.

A wind-driven triboelectric nanogenerator (W-TENG) is a promising energy harvesting device due to its clean, ubiquitous and unexhausted properties. In addition, a W-TENG induces unpredictable chaotic outputs from wind flow that can serve as an entropy source for cryptography. This can be applied to a true random number generator (TRNG) for a secured system due to its inherent turbulent nature; thus, a W-TENG with a two-in-one structure can simultaneously generate both power and true random numbers. However, a previously reported W-TENG had one major drawback: a wind velocity of 10 m/s is required for stable energy harvesting by wind force. Thus, it is timely to demonstrate a W-TENG-based RNG whose operating condition is below 3 m/s, which is a gentle breeze similar to natural wind. In this study, we demonstrate a wind-driven cryptographic triboelectric random number generator (WCT-RNG) by using a W-TENG whose operating condition for wind speed is below 3 m/s by adopting a rear-fixed film structure instead of a conventional structure. The rear-fixed film refers to the fluttering film being freestanding on the front-side and fixed on the rear-side, where the front- and rear-sides are the wind inlet and outlet, respectively. The WCT-RNG enables the W-TENG to operate below a 3 m/s wind velocity. Because of this, the working time of the WCT-RNG is dramatically enhanced from only 8-42% at an average altitude above sea level. As the capability of operating at low wind speeds is significantly improved, a WCT-RNG becomes more useful and practical for generating both power and true random numbers in a single device. The device can thereby lead to the construction of a self-powered TRNG and secure communication for Internet of Things (IoT) devices in various environments, even under a gentle breeze. In this study, we explain the design of a WCT-RNG structure and also evaluate its randomness by using an NIST SP 800-22 B test suite with a reliability test.

Cryptographic transistor for true random number generator with low power consumption.

We design a cryptographic transistor (cryptoristor)-based true random number generator (tRNG) with low power consumption and small footprint. This is the first attempt to use irregular and unpredictable operation-induced randomness of a cryptoristor as an entropy source. To extract discrete random numbers with a binary code from the cryptoristor, we developed a noise-coupling analog-to-digital converter. This converter not only converts analog signals to digital random bits but also improves the randomness of the entropy source with low power consumption. The randomness of the cryptoristor is attributed to the random carrier multiplications with the creation and stochastic carrier escape as destruction, which occurs iteratively as long as the input current is fed. The cryptoristor-based tRNG passed 15 randomness test suites of NIST Special Publication 800-22. It is robust to iterative operational stresses and to ambient temperature changes, making it an attractive option for hardware-based security solutions in the Internet of Things due to its low power consumption and small size.

Spike-Based Neuromorphic Hardware for Dynamic Tactile Perception with a Self-Powered Mechanoreceptor Array.

A self-powered mechanoreceptor array is demonstrated using four mechanoreceptor cells for recognition of dynamic touch gestures. Each cell consists of a triboelectric nanogenerator (TENG) for touch sensing and a bi-stable resistor (biristor) for spike encoding. It produces informative spike signals by sensing a force of an external touch and encoding the force into the number of spikes. An array of the mechanoreceptor cells is utilized to monitor various touch gestures and it successfully generated spike signals corresponding to all the gestures. To validate the practicality of the mechanoreceptor array, a spiking neural network (SNN), highly attractive for power consumption compared to the conventional von Neumann architecture, is used for the identification of touch gestures. The measured spiking signals are reflected as inputs for the SNN simulations. Consequently, touch gestures are classified with a high accuracy rate of 92.5%. The proposed mechanoreceptor array emerges as a promising candidate for a building block of tactile in-sensor computing in the era of the Internet of Things (IoT), due to the low cost and high manufacturability of the TENG. This eliminates the need for a power supply, coupled with the intrinsic high throughput of the Si-based biristor employing complementary metal-oxide-semiconductor (CMOS) technology.

Self-Mixed Biphasic Liquid Metal Composite with Ultra-High Stretchability and Strain-Insensitivity for Neuromorphic Circuits.

Neuromorphic circuits that can function under extreme deformations are important for various data-driven wearable and robotic applications. Herein, biphasic liquid metal particle (BMP) with unprecedented stretchability and strain-insensitivity (ΔR/R0 = 1.4@ 1200% strain) is developed to realize a stretchable neuromorphic circuit that mimics a spike-based biologic sensory system. The BMP consists of liquid metal particles (LMPs) and rigid liquid metal particles (RLMPs), which are homogeneously mixed via spontaneous solutal-Marangoni mixing flow during coating. This permits facile single step patterning directly on various substrates at room temperature. BMP is highly conductive (2.3 × 106 S/m) without any post activation steps. BMP interconnects are utilized for a sensory system, which is capable of distinguishing variations of biaxial strains with a spiking neural network, thus demonstrating their potential for various sensing and signal processing applications.