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-tosystem 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.The mammalian neocortex offers extremely energy-efficient information processing performance in tasks such as pattern recognition with a power consumption of only 10-20 watts 1 . By mimicking both the functional and structural advantages of this biological neural system, the recent development of power-efficient computing systems, i.e., neuromorphic systems (hardware neural networks) 2 , has been expected to offer a promising breakthrough for applications, ranging from mobile platforms 3 to artificial intelligence operations 4 , where power consumption is a concern.A unique feature of neuromorphic systems is efficient parallel data processing, where the processing of information can be performed by modulating the connection strength of synapses (referred to as the synaptic weight) 5 . This synaptic weight can be modulated by either potentiating or depressing neural spikes (pulses) from pre-and post-synaptic neurons, following appropriate learning rules, such as spike-timing-dependent plasticity (STDP) 6 . Therefore, a key element in the neuromorphic system is the implementation of an ideal synaptic device that can emulate the functionality of biological synapses.To date, various nano-electronic devices have successfully reproduced a specific learning rule of biological synapses through their internal analog conductance states that can be modulated intentionally with an applied pulse's timing or level [7][8][9][10][11][12][13][14][15] . Moreover, the potential of ultralow energy consumption per synaptic operation 16 , as well as the possibility of realizing three-dimensional integration 17 , have shown the promising feasibility of large-scale neuromorphic system implementation in the near future. However, the sustainability of such devices is still in dou...

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-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 10. 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.

Carbon nanotubes (CNTs) are emerging materials for semiconducting channels in high-performance thin-film transistor (TFT) technology. However, there are concerns regarding the contact resistance (Rcontact) in CNT-TFTs, which limits the ultimate performance, especially the CNT-TFTs with the inkjet-printed source/drain (S/D) electrodes. Thus, the contact interfaces comprising the overlap between CNTs and metal S/D electrodes play a particularly dominant role in determining the performances and degree of variability in the CNT-TFTs with inkjet-printed S/D electrodes. In this work, the CNT-TFTs with improved device performance are demonstrated to enhance contact interfaces by controlling the CNT density at the network channel and underneath the inkjet-printed S/D electrodes during the formation of a CNT network channel. The origin of the improved device performance was systematically investigated by extracting Rcontact in the CNT-TFTs with the enhanced contact interfaces by depositing a high density of CNTs underneath the S/D electrodes, resulting in a 59% reduction in Rcontact; hence, the key performance metrics were correspondingly improved without sacrificing any other device metrics.

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.