# Recent Advances in Fiber-Shaped Electronic Devices for Wearable Applications
## 3 Device Applications
### Electronic Devices
#### 3.1.3 Computing Units  
Machine learning for wearable applications is becoming a popular way to study the huge quantities of data accrued during the last few years [37]. Despite advances in fiber devices based on AI, such devices typically depend on cloud servers and physically separated computing systems. In other words, the mass of data generated from electronic fibers or e-textiles should be transferred to another computing component. That process consumes a large amount of energy and time. Therefore, a smart e-textile system that can implement data processing or machine learning is highly desirable [38,39]. One potential method would be to integrate computing fibers within the 1D functional devices [40]. For example, [PERSON] et al. reported interwoven textile memristors compatible with a two-terminal crossbar configuration (Figure 4a) [41]. Deoxyribonucleic acid (DNA) molecules incorporating Ag nanoparticles as a bio-compatible and ionic conducting layer were assembled on metal fibers via electrophoretic deposition [41]. Interlaced with Pt fiber electrodes, the DNA active layer functioned as a textile memristor and had excellent memristive characteristics: low operating voltage (about 0.3 V), fast switching time (20 ns), and low power consumption (about 100 pW) [41]. The edge-on orientation of DNA molecules provided efficient transport pathways for Ag\({}^{+}\) ions, enabling robust performance [41]. These memristive textiles implemented fundamental logic calculations such as implication and NAND, and were integrated with a textile power source and light-emitting modules to demonstrate the feasibility of a wearable information processing system (Figure 4b) [41]. To bypass the [PERSON] bottleneck and avoid high power consumption, bioinspired computing systems have been developed and are considered to be a promising alternative to traditional hardware. Research on artificial synapses, inspired by the function  
Figure 3: (**a**) Schematic of the device structure and the retention property of a fiber-shaped memory device (**b**) Endurance test of a 3 \(\times\) 3 RRAM array embroidered into a fabric. (**a**,**b**) Reproduced with permission Ref. [34]. Copyright 2017, Wiley-VCH. (**c**) Schematic illustration of the deposition of pEGDMA using an iCVD process. (**d**) Left: schematic of the logic-in-memory operations of NOR gate and its equivalent circuits within the crossbar array. The inset presents a conceptual image for the integration of logic gates on the fabric. Right: measurements of the NOR gate. (**c**,**d**) Reproduced with permission Ref. [35]. Copyright 2017, American Chemical Society. (**e**) Schematic illustration of a continuous reel-to-reel coating process and the device structure of the FOMs. (**f**) Memory and bending characteristics of the FOMs. (**e**,**f**) Reproduced with permission Ref. [3]. Copyright 2019, American Chemical Society. and structure of the human brain, is constantly being conducted to achieve the required neural computations [42]. So far, two-terminal and three-terminal fiber-shaped artificial synapses have been demonstrated. [PERSON] et al. fabricated two-terminal memristor-type artificial synapses comprising two yarns coated with reduced graphene oxide (rGO) by electrochemical deposition [43]. The rGO-based artificial synapses emulated several synaptic functions, such as paired-pulse facilitation, excitatory postsynaptic current, and a transition from short-term plasticity to long-term plasticity [43]. The yarn-based synapses were assembled in a cross-point structure for e-textile application [43]. However, in an integrated synaptic array, the two-terminal structure can induce undesirable neural signals across neighboring nodes, leading to the degradation of learning accuracy [44]. To prevent such cell-to-cell crosstalk and improve the reliability of the learning capability, 1D organic synaptic transistors based on a three-terminal device structure were constructed by [PERSON] et al. (Figure 4c) [45].