Mapping Sensitivity of Nanomaterial Field-Effect Transistors

Abstract

As society becomes increasingly data-driven, the appetite of individuals, corporations, and algorithms for data sources swells, strengthening the demand for sensors. Chemical sensors are of particular interest as they provide highly human-relevant information, such as DNA sequences, cancer biomarker concentrations, blood glucose levels, antibody detection, and viral testing, to name a few. Among the most promising transduction elements for chemical sensors are nanomaterial field-effect transistors (FETs). The nanoscale size of these devices allows them to operate using very small sample sizes (an extremely small volume of patient blood, for instance), be strongly influenced by low concentrations of the target chemical, and be produced at low-cost, potentially using the same methods developed for consumer electronics (which have achieved a cost of less than 0.000001 cents per device). Nanomaterial FET-based chemical sensors also have the advantage of directly transducing a chemical presence or change to an electrical output signal. This avoids components such as lasers, optics, fluorophores, and more, that are frequently used as a part of the transduction chain in other types of chemical sensors, adding size, complexity, and cost. Much work has focused on demonstrating one-off nanomaterial FET-based sensors, but less work has been done to determine the underlying mechanisms that lead to sensitivity by mapping sensitivity against other variables in experimental devices. With challenges of consistency and reproducible operation stifling progress in this field, there is a significant need to improve understanding of nanomaterial-based FET sensitivity and operation mechanisms.The work contained in this dissertation maps the sensitivity of nanomaterial FETs across a range of parameters, including space, time, device operating point, and analyte charge. This mapping is performed in an effort to yield insight into the underlying mechanisms that govern the sensitivity of these devices to nearby charges. In order to both draw comparisons between different device types and to make the results of this work broadly applicable to the field as a whole, four types of devices were studied that span a broad range of characteristics. The device types spanned from channels of one-dimensional nanotubes to three-dimensional nanostructures, and from partially printed fabrication to cleanroom-based nanofabrication. Specifically, the devices explored herein are carbon nanotube (CNT) FETs, molybdenum disulfide (MoS2) FETs, silicon nanowire FETs, and carbon nanotube thin-film transistors (CNT-TFTs). Fabrication processes were developed to build devices of each of these types that are capable of undergoing long-term electronic testing with reliable contact strategies. Passivation schemes were also developed for each device type to enable testing in solution and formation of solution-based sensors so that results could be extended to the case of biosensors. An automated experimentation platform was developed to enable tight synchronization between characterization instruments so that each variable impacting device sensitivity could be controlled and measured in tandem, in some cases for months on end.Many of the obtained results showed similar trends in sensitivity between device types, while some findings were unique to a given channel material. All tested devices showed stability after a period of drain current settling caused by the occupation equilibration of charge trap states – an effect that was found to severely reduce sensitivity and dynamic range. For CNTs specifically, two new decay modes were discovered (intermediate between device stability and breakdown) along with respective onset voltages that can be used to avoid them. For CNT-TFTs, it was found that the relationship between signal-to-noise ratio (SNR) and device operating point remained consistent between ambient air and solution environments, indicating that this relationship is governed primarily by properties of the device. A simple chemical sensor made from the same devices showed a clear peak in the SNR near the device threshold voltage – a result that became increasingly meaningful when combined with similar observations in other device types obtained via separate experimental methods. For both silicon nanowire and MoS2 FETs, sensitivity was mapped in space with sub-nanometer precise control over analyte position. Both device types manifested distinct sensitivity hotspots spread across the geometry of the channel. These hotspots were found to be stable in time, but their prominence depended heavily on the device operating point. When SNR was mapped across a range of operating points for these devices, a clear peak was discovered, with the hotspot intensity culminating at the peak. Ideal operating points were identified to be near the threshold voltage for both device types, with findings (and a developed numerical model) in MoS2 indicating that the operating point where SNR is maximized may depend upon the extent of the channel that is influenced by the analyte. Observations from multiple devices and approaches revealed that SNR peaks below the point of maximal transconductance, offering increased resolution to a matter that has previously been of some debate in the literature. In MoS2 FETs, a significant asymmetry was discovered in the response of devices to analytes of opposing polarity, with analytes that modulate devices toward their off-state eliciting a much larger response (and, correspondingly, SNR). This asymmetry was confirmed by a numerical model that suggested it to be a general result applicable to all FET-based charge detection sensors, leading to the recommendation that sensor designers select FETs that will be turned off by the target analyte.Each finding contributed by this dissertation provides insight into future sensor designs and increases clarity of the underlying mechanisms leading to sensitivity in nanomaterial FET-based sensors. The discovery of decay modes, hotspots, ideal operating points, asymmetries, and other trends comprise substantial scientific advancements and propel the field closer to the goal of providing ubiquitous access to critical information, diagnoses, and measurements that promptly and correctly inform decisions.