There is an increasing interest in the development of memristive or artificial synaptic devices that emulate the neuronal activities for neuromorphic computing applications. While there have already been many reports on artificial synaptic transistors implemented on rigid substrates, the use of flexible devices could potentially enable an even broader range of applications. In this paper, we report artificial synaptic thinfilm transistors built on an ultrathin flexible substrate using high carrier mobility semiconducting single-wall carbon nanotubes. The synaptic characteristics of the flexible synaptic transistor including long-term/short-term plasticity, spikeamplitude-dependent plasticity, spike-width-dependent plasticity, paired-pulse facilitation, and spike-time-dependent plasticity have all been systematically characterized. Furthermore, we have demonstrated a flexible neurological electronic skin and its peripheral nerve with a flexible ferroelectret nanogenerator (FENG) serving as the sensory mechanoreceptor that generates action potentials to be processed and transmitted by the artificial synapse. In such neurological electronic skin, the flexible FENG sensor converts the tactile input (magnitude and frequency of force) into presynaptic action potential pulses, which are then passed to the gate of the synaptic transistor to induce change in its postsynaptic current, mimicking the modulation of synaptic weight in a biological synapse. Our neurological electronic skin closely imitates the behavior of actual human skin, and it allows for instantaneous detection of force stimuli and offers biological synapse-like behavior to relay the stimulus signals to the next stage. The flexible sensory skin could potentially be used to interface with skeletal muscle fibers for applications in neuroprosthetic devices.

As the initial stage in the formation of human intelligence, the sensory–memory system plays a critical role for human being to perceive, interact, and evolve with the environment. Electronic implementation of such biological sensory–memory system empowers the development of environment-interactive artificial intelligence (AI) that can learn and evolve with diversified external information, which could potentially broaden the application of the AI technology in the field of human–computer interaction. Here, we report a multimodal artificial sensory–memory system consisting of sensors for generating biomimetic visual, auditory, tactile inputs, and flexible carbon nanotube synaptic transistor that possesses synapse-like signal processing and memorizing behaviors. The transduction of physical signals into information-containing, presynaptic action potentials and the synaptic plasticity of the transistor in response to single and long-term action potential excitations have been systematically characterized. The bioreceptor-like sensing and synapse-like memorizing behaviors have also been demonstrated. On the basis of the memory and learning characteristics of the sensory–memory system, the well-known psychological model describing human memory, the “multistore memory” model, and the classical conditioning experiment that demonstrates the associative learning of brain, “Pavlov’s dog’s experiment”, have both been implemented electronically using actual physical input signals as the sources of the stimuli. The biomimetic intelligence demonstrated in this neurological sensory–memory system shows its potential in promoting the advancement in multimodal, user-environment interactive AI.

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Applying flexible materials for energy scavenging from ambient mechanical vibrations is a clean energy solution that can help alleviate electrical power demands in portable devices and wearable electronics. This work presents fundamental studies on a flexible ferroelectret polymer with a strong piezoelectric effect and its interface with self-powered and energy storage systems. A single-layered device with a thickness of 80 μm was used for characterizing the device’s output voltage, current, transferred charge, and energy conversion efficiency. The potential capability of harvesting mechanical energy and delivering to system load is demonstrated by integrating the device into a fully integrated power management system. The theory for determining the harvested energy that is ultimately delivered to external electronic loads (or stored in a battery) is discussed. The maximum power delivery is found to be for a 600 MΩ load, which results in a device power density of 14.0 W/m3 for input mechanical forces with a frequency around 2 Hz.

The recent development on wearable and stretchable electronics calls for skin conformable power sources that are beyond current battery technologies. Among the many novel energy devices being explored, triboelectric nanogenerator (TENG) made from intrinsically stretchable materials has a great potential to meet the above requirement as being both soft and efficient. In this paper, we present a lithography-free and low-cost TENG device comprising a porous-structured PDMS layer and a stretchable PEDOT:PSS electrode. The porous PDMS structure is formed by using self-assembled polystyrene beads as the sacrificial template and it is highly ordered with great uniformity and high structural stability under compression force. Moreover, the porous PDMS TENG exhibits improved output voltage and current of 1.65 V and 0.54 nA compared to its counterpart with non-porous PDMS with 0.66 V and 0.34 nA. The effect of different loading force and frequency on the output response of the TENG device has also been studied. This work could shed light on diverse structural modification methods for improving the performance of PDMS-based TENG and the development of intrinsically stretchable TENG for wearable device applications.