We report on a pH sensor with ultrahigh sensitivity based on a microcantilever structure with a lithographically-defined crosslinked copolymeric hydrogel. Silicon-on-insulator wafers were used to fabricate cantilevers on which a polymer consisting of poly(methacrylic acid) (PMAA) with poly(ethylene glycol) dimethacrylate was patterned using free-radical UV polymerization. As the pH around the cantilever was increased above the pKa of PMAA, the polymer network expanded and resulted in a reversible change in surface stress causing the microcantilever to bend. Excellent mechanical amplification of polymer swelling as a function of pH change within the dynamic range was obtained, with a maximum deflection sensitivity of 1 nm/5×10−5 ΔpH.

Semiconductor device-based sensing of chemical and biological entities has been demonstrated through the use of micro- and nanoscale field-effect devices and close variants. Although carbon nanotubes and silicon nanowires have been demonstrated as single molecule biosensors, the fabrication methods that have been used for creating these devices are typically not compatible with modern semiconductor manufacturing techniques and their large scale integration is problematic. These shortcomings are addressed by recent advancements in microelectronic fabrication techniques which resulted in the realization of nanowire-like structures. Here we report a method to fabricate silicon nanowires at precise locations using such techniques. Our method allows for the realization of truly integrated sensors capable of production of dense arrays. Sensitivity of these devices to changes in the ambient gas composition is also shown.

With the success of deep learning in a wide variety of areas, many deep multi-task learning (MTL) models have been proposed claiming improvements in performance obtained by sharing the learned structure across several related tasks. However, the dynamics of multi-task learning in deep neural networks is still not well understood at either the theoretical or experimental level. In particular, the usefulness of different task pairs is not known a priori. Practically, this means that properly combining the losses of different tasks becomes a critical issue in multi-task learning, as different methods may yield different results. In this paper, we benchmarked different multi-task learning approaches using shared trunk with task specific branches architecture across three different MTL datasets. For the first dataset, i.e. Multi-MNIST (Modified National Institute of Standards and Technology database), we thoroughly tested several weighting strategies, including simply adding task-specific cost functions together, dynamic weight average (DWA) and uncertainty weighting methods each with various amounts of training data per-task. We find that multitask learning typically does not improve performance for a user-defined combination of tasks. Further experiments evaluated on diverse tasks and network architectures on various datasets suggested that multitask learning requires careful selection of both task pairs and weighting strategies to equal or exceed the performance of single task learning.INDEX TERMS Dynamic weighting average, multi-MNIST, multi-objective optimization, multi-task learning, uncertainty weighting.

Over the last decade, field-effect transistors (FETs) with nanoscale dimensions have emerged as possible label-free biological and chemical sensors capable of highly sensitive detection of various entities and processes. While significant progress has been made towards improving their sensitivity, much is yet to be explored in the study of various critical parameters, such as the choice of a sensing dielectric, the choice of applied front and back gate biases, the design of the device dimensions, and many others. In this work, we present a process to fabricate nanowire and nanoplate FETs with Al2O3 gate dielectrics and we compare these devices with FETs with SiO2 gate dielectrics. The use of a high-k dielectric such as Al2O3 allows for the physical thickness of the gate dielectric to be thicker without losing sensitivity to charge, which then reduces leakage currents and results in devices that are highly robust in fluid. This optimized process results in devices stable for up to 8 h in fluidic environments. Using pH sensing as a benchmark, we show the importance of optimizing the device bias, particularly the back gate bias which modulates the effective channel thickness. We also demonstrate that devices with Al2O3 gate dielectrics exhibit superior sensitivity to pH when compared to devices with SiO2 gate dielectrics. Finally, we show that when the effective electrical silicon channel thickness is on the order of the Debye length, device response to pH is virtually independent of device width. These silicon FET sensors could become integral components of future silicon based Lab on Chip systems.

In this work, we report on the optimization of a double-gate silicon-on-insulator field effect device operation to maximize pH sensitivity. The operating point can be fine tuned by independently biasing the fluid and the back gate of the device. Choosing the bias points such that device is nearly depleted results in an exponential current response—in our case, 0.70decade per unit change in pH. This value is comparable to results obtained with devices that have been further scaled in width, reported at the forefront of the field, and close to the ideal value of 1decade∕pH. By using a thin active area, sensitivity is increased due to increased coupling between the two conducting surfaces of the devices.