Micron-scale organic thin film transistors with conducting polymer electrodes patterned by polymer inking and stamping

We report organic thin film transistors (OTFTs) with conductive polymer poly (3,4-ethylenedioxythiophene)/poly(4-styrenesulphonate) (PEDOT) electrodes that are fabricated by a simple polymer inking and stamping technique. An OTFT channel length of 2 μm2μm has been achieved. This patterning technique is a purely additive process, which does not affect the functionality of the conductive polymers, and is fully compatible for patterning on a flexible substrate. Electrical characteristics of top contact (TC) pentacene TFTs with PEDOT electrodes is superior to those with gold electrodes due to a lower carrier injection barrier. Extracted contact resistance shows that the channel length of TC OTFTs can be further reduced to increase the drain current.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87779/2/063513_1.pd

Organic thin film transistors and polymer light-emitting diodes patterned by polymer inking and stamping

"To fully realize the advantages of organic flexible electronics, patterning is very important. In this paper we show that a purely additive patterning technique, termed polymer inking and stamping, can be used to pattern conductive polymer PEDOT and fabricate sub-micron channel length organic thin film transistors. In addition, we applied the technique to transfer a stack of metal/conjugated polymer in one step and fabricated working polymer light-emitting devices. Based on the polymer inking and stamping technique, a roll-to-roll printing for high throughput fabrication has been demonstrated. We investigated and explained the mechanism of this process based on the interfacial energy consideration and by using the finite element analysis. This technique can be further extended to transfer more complex stacked layer structures, which may benefit the research on patterning on flexible substrates."http://deepblue.lib.umich.edu/bitstream/2027.42/64207/1/d8_10_105115.pd

Transistor performance of top rough surface of pentacene measured by laminated double insulated-gate supported on a poly(dimethylsiloxanes) base structure

We report the fabrication and electrical characterization of pentacene field-effect transistors with a laminated double insulated-gate using poly(dimethylsiloxanes) (PDMS) as their supporting structure. The ability of PDMS to conform to surfaces enables us to directly evaluate the device performance of the top rough surface of the pentacene active layer (the pentacene-air interface). The mobility measured for the top surface was only about 20% slightly lower than that of the bottom surface. Device stability under ambient conditions is evaluated. This device structure is useful for the characterization of electrical transport in both the top and bottom surface of a thin film simultaneously.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87784/2/033502_1.pd

A MapReduce-based nearest neighbor approach for big-data-driven traffic flow prediction

In big-data-driven traffic flow prediction systems, the robustness of prediction performance depends on accuracy and timeliness. This paper presents a new MapReduce-based nearest neighbor (NN) approach for traffic flow prediction using correlation analysis (TFPC) on a Hadoop platform. In particular, we develop a real-time prediction system including two key modules, i.e., offline distributed training (ODT) and online parallel prediction (OPP). Moreover, we build a parallel k-nearest neighbor optimization classifier, which incorporates correlation information among traffic flows into the classification process. Finally, we propose a novel prediction calculation method, combining the current data observed in OPP and the classification results obtained from large-scale historical data in ODT, to generate traffic flow prediction in real time. The empirical study on real-world traffic flow big data using the leave-one-out cross validation method shows that TFPC significantly outperforms four state-of-the-art prediction approaches, i.e., autoregressive integrated moving average, Naïve Bayes, multilayer perceptron neural networks, and NN regression, in terms of accuracy, which can be improved 90.07% in the best case, with an average mean absolute percent error of 5.53%. In addition, it displays excellent speedup, scaleup, and sizeup

Resilient neural network training for accelerators with computing errors

—With the advancements of neural networks, customized accelerators are increasingly adopted in massive AI applications. To gain higher energy efficiency or performance, many hardware design optimizations such as near-threshold logic or overclocking can be utilized. In these cases, computing errors may happen and the computing errors are difficult to be captured by conventional training on general purposed processors (GPPs). Applying the offline trained neural network models to the accelerators with errors directly may lead to considerable prediction accuracy loss. To address this problem, we explore the resilience of neural network models and relax the accelerator design constraints to enable aggressive design options. First of all, we propose to train the neural network models using the accelerators’ forward computing results such that the models can learn both the data and the computing errors. In addition, we observe that some of the neural network layers are more sensitive to the computing errors. With this observation, we schedule the most sensitive layer to the attached GPP to reduce the negative influence of the computing errors. According to the experiments, the neural network models obtained from the proposed training outperform the original models significantly when the CNN accelerators are affected by computing errors

An efficient MapReduce-based parallel clustering algorithm for distributed traffic subarea division

Traffic subarea division is vital for traffic system management and traffic network analysis in intelligent transportation systems (ITSs). Since existing methods may not be suitable for big traffic data processing, this paper presents a MapReduce-based Parallel Three-Phase K -Means (Par3PKM) algorithm for solving traffic subarea division problem on a widely adopted Hadoop distributed computing platform. Specifically, we first modify the distance metric and initialization strategy of K -Means and then employ a MapReduce paradigm to redesign the optimized K -Means algorithm for parallel clustering of large-scale taxi trajectories. Moreover, we propose a boundary identifying method to connect the borders of clustering results for each cluster. Finally, we divide traffic subarea of Beijing based on real-world trajectory data sets generated by 12,000 taxis in a period of one month using the proposed approach. Experimental evaluation results indicate that when compared with K -Means, Par2PK-Means, and ParCLARA, Par3PKM achieves higher efficiency, more accuracy, and better scalability and can effectively divide traffic subarea with big taxi trajectory data

Facile Solution Process of VO2 Film with Mesh Morphology for Enhanced Thermochromic Performance

The fabrication and applications of VO2 film continue to be of considerable interest due to their good thermochromic performance for smart windows. However, low visible transmittance (Tlum) and solar modulation efficiency (∆Tsol) impede the application of VO2 film, and they are difficult to improve simultaneously. Here, a facile zinc solution process was employed to control the surface structure of dense VO2 film and the processed VO2 film showed enhanced visible transmittance and solar modulation efficiency, which were increased by 7.5% and 9.5%, respectively, compared with unprocessed VO2 film. This process facilitated the growth of layered basic zinc acetate (LBZA) nanosheets to form mesh morphology on the surface of VO2 film, where LBZA nanosheets enhance the visible transmittance as an anti-reflection film. The mesh morphology also strengthened the solar modulation efficiency with small caves between nanosheets by multiplying the times of reflection. By increasing the zinc concentration from 0.05 mol/L to 0.20 mol/L, there were more LBZA nanosheets on the surface of the VO2 film, leading to an increase in the solar/near-infrared modulation efficiency. Therefore, this work revealed the relationship between the solution process, surface structure, and optical properties, and thus can provide a new method to prepare VO2 composite film with desirable performance for applications in smart windows