Design of a Power Amplifier and Envelope Amplifier for a Multi-band Multi-standard Envelope Tracking System

This thesis presents the design of a Power Amplifier (PA) and envelope amplifier for an Envelope Tracking (ET) system that is aimed at meeting emerging radio standards in terms of power efficiency and linearity. The class J mode of operation, as well as the efficiency and power contours from load pull was exploited to develop an adequate procedure for the design of a broadband and high efficiency radio frequency PA. An in-depth study has also been conducted for a hybrid envelope amplifier topology in order to optimize it for power efficiency through proper setting of its switching stage supply. Two separate proof of concept prototypes of the PA and envelop amplifier were designed, fabricated and tested. The PA designed was able to achieve an average drain efficiency of 73.6%, average output power of 45.89dBm, and an average gain of 18dB between 650MHz and 1.050GHz (48% bandwidth). The envelope amplifier achieved close to 74.6% efficiency for a 5MHz bandwidth LTE signal envelope with 6.4dB peak to average power ratio

A Novel Power-Scalable Wideband Power Amplifier Linearization Technique

Global mobile traffic is expected to continue to increase at an astonishing rate in the future, due to the ever-increasing number of mobile phone subscribers and the adoption of smart devices which generate significantly more mobile traffic. To satisfy this growth in demand, it is envisioned that future 5th Generation (5G) mobile networks will utilize lower powered small-cell base stations and base stations with large antenna arrays to greatly improve network coverage and capacity. A power amplifier (PA) is a critical component in a base station’s transmitter, required to boost the signal power such that it is high enough for transmission to the intended receiver. The design of the PA for 5G base stations, however, presents new challenges to designers. When driven with modern wideband communication signals, the PA must be both efficient and linear in order to minimize power consumption, improve reliability, maintain transmission accuracy, and avoid interference with neighbouring signals. In conventional high-powered macrocell base station designs, the aforementioned requirements are usually satisfied using a two-step procedure. First, the PA is designed using a Doherty power amplifier (DPA) topology, which has high efficiency, but poor linearity. Then, digital predistortion (DPD) linearization techniques are applied to ensure that the DPA attains the required linearity performance. However, for the lower-powered PAs needed in small cells and large antenna arrays, the relatively high power overhead of DPD techniques, which does not scale down as the power range of the PA decreases, make them unattractive PA linearization solutions. In response, a new PA linearization technique is proposed and developed in this thesis. It is based on the design and addition of a linearization amplifier (LA), an approach which can help the PA attain the required linearity even when it is driven with modern communication signals with very wide bandwidths. Of particular note, the LA’s power consumption is relatively low, it scales with the PA’s power range, and it does not increase with signal bandwidth. These qualities make it highly suitable for use with PAs in future 5G small-cell base stations and base stations with large antenna arrays. To validate the proposed technique’s effectiveness, a prototype circuit was designed, fabricated and applied to a high peak efficiency 6 W class AB PA with a centre frequency of 850 MHz. When stimulated by a wideband 40 MHz signal, the PA’s adjacent channel leakage ratio (ACLR) was improved by up to 13 dB after the addition of the LA. This enabled the PA to achieve an ACLR of about -45 dBc without the use of any other linearization techniques. Significant ACLR improvements were also observed for signals with even wider bandwidths of up to 160 MHz. Moreover, it was shown that the LA could be used in conjunction with a simple predistorter to further improve the efficiency and linearity of the class AB PA. Next, the LA is augmented with a conventional DPA design to form a new linear DPA topology that was able to achieve a better linearity-efficiency trade-off compared to the linearized class AB PA. To accomplish this, a study of the interactions between the LA and DPA circuitries was conducted and a design strategy was developed to determine the circuit parameters that maximized ACLR improvement while minimizing power consumption. For validation purposes, this strategy was applied to design a proof-of-concept prototype with a centre frequency of 800 MHz and a peak envelope power of 12 W. With the addition of the LA, a more than 11 dB improvement of the ACLR was obtained at the prototype’s output when it was driven with signals with up to 40 MHz of modulation bandwidth: an ACLR of about -45 dBc or better was achieved over wide average power range. As expected, the efficiency of the linear DPA topology remained significantly higher than the linearized class AB PA for all signals tested. Another challenge faced in particular by PAs in a large antenna array, is that it will experience dynamic load impedance variations due to antenna coupling. This unwanted variation in the load impedance can cause instability and significant distortions at the output of the PA that is difficult to remedy using conventional techniques. To address these issues, it is shown in the last part of this thesis that the LA can be used to mitigate this problem by minimizing the amount of load impedance variation seen by the PA due to antenna coupling, such that it remains closer to its optimal value, and by maintaining excellent linearization across a wide range of load impedance values

Single Palladium Nanowire: Synthesis Via Electrophoresis Deposition and Hydrogen Sensing

Palladium (Pd) single nanowires with less than 100 nm thickness have been successfully and repeatedly fabricated using electrophoresis deposition method. This work also demonstrates the use of a single Pd nanowire as a hydrogen sensor with extremely high sensitivity. Growth and sensing mechanisms are discussed in detail and an improved synthesis method is proposed and proved.Single Pd nanowires were electrodeposited within 100 nm wide Poly-Methyl-Methacrylate (PMMA) nanochannels by using a probe station. The PMMA channels were fabricated by Electron Beam Lithography (EBL) on a pre-patterned template fabricated via photolithography and lift-off processes. Through this method, nanowires were grown with widths ranging from 50 nm to 100 nm, and lengths from 3.5 ¦Ìm to 6.5 ¦Ìm. The nanowires, successfully integrated into hydrogen sensor devices, were able to sense hydrogen concentrations as low as 5 ppm (part per million) at room temperature. Three different nanowire structures were found, and the growth control of nanowire structure was conducted. Different sensing mechanisms were addressed in detail according to different nanowire structures.A newly developed gate assisted growth method were also proposed and approved in this work. Thinner and more uniform nanowires were synthesized with this method, and the growth time was greatly reduced. Experimental data were presented to approve the effects of gate voltage on nanowire diameter and growth time

Electrochemically-Grown Single Nanowire Array for Highly Sensitive and Selective Chemical Detection

One dimensional nanostructures (nanowires) have emerged as important building blocks for micro/nano devices, such as chemical and biomolecular sensors, photovoltaic devices, nonvolatile memories, and nano power generators. In this work, the fabrication and characterization of single metal, conducting polymer and metal oxide nanowires will be discussed. These single nanowires were synthesized site-specifically inside Polymethyl methacrylate (PMMA) channels defined by electron beam lithography (EBL) via electrochemical deposition. The dimensions of these nanowires were predefined by optical lithography and EBL, and the widths were from 100 nm to 50 nm and the lengths were from 3 to 7 microns. A gate-assisted electrochemical deposition method that was able to effectively improve the nanowire growth will be discussed. The successful integration of four different single nanowires on a single chip will also be demonstrated.The applications of these single nanowires will be presented. A highly sensitive hydrogen sensor with fast response (<20 s) and extremely low detection limit (2 ppm) was achieved using single Palladium (Pd) nanowire. The structure of the Pd nanowire was found to be closely related to the growth condition, and different sensing mechanisms were discovered for different nanowire structures.An electronic nose was built on a single chip using a nanowire array consisting of four different single nanowires, including Pd, Polypyrrole (PPy), Polyaniline (PANI), and ZnO nanowires. The sensing performances of this electronic nose for four different target gases, including carbon monoxide (CO), hydrogen (H2), nitrogen dioxide (NO2), and methanol (CH3OH), were studied in detail. Principal Component Analysis (PCA) was employed to analyze the sensing signals and each target gas was clearly identified. A blind test was conducted to verify the performance of this e-nose

TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering

Despite thousands of researchers, engineers, and artists actively working on improving text-to-image generation models, systems often fail to produce images that accurately align with the text inputs. We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA). Specifically, given a text input, we automatically generate several question-answer pairs using a language model. We calculate image faithfulness by checking whether existing VQA models can answer these questions using the generated image. TIFA is a reference-free metric that allows for fine-grained and interpretable evaluations of generated images. TIFA also has better correlations with human judgments than existing metrics. Based on this approach, we introduce TIFA v1.0, a benchmark consisting of 4K diverse text inputs and 25K questions across 12 categories (object, counting, etc.). We present a comprehensive evaluation of existing text-to-image models using TIFA v1.0 and highlight the limitations and challenges of current models. For instance, we find that current text-to-image models, despite doing well on color and material, still struggle in counting, spatial relations, and composing multiple objects. We hope our benchmark will help carefully measure the research progress in text-to-image synthesis and provide valuable insights for further research

Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation

Evaluating text-to-image models is notoriously difficult. A strong recent approach for assessing text-image faithfulness is based on QG/A (question generation and answering), which uses pre-trained foundational models to automatically generate a set of questions and answers from the prompt, and output images are scored based on whether these answers extracted with a visual question answering model are consistent with the prompt-based answers. This kind of evaluation is naturally dependent on the quality of the underlying QG and QA models. We identify and address several reliability challenges in existing QG/A work: (a) QG questions should respect the prompt (avoiding hallucinations, duplications, and omissions) and (b) VQA answers should be consistent (not asserting that there is no motorcycle in an image while also claiming the motorcycle is blue). We address these issues with Davidsonian Scene Graph (DSG), an empirically grounded evaluation framework inspired by formal semantics. DSG is an automatic, graph-based QG/A that is modularly implemented to be adaptable to any QG/A module. DSG produces atomic and unique questions organized in dependency graphs, which (i) ensure appropriate semantic coverage and (ii) sidestep inconsistent answers. With extensive experimentation and human evaluation on a range of model configurations (LLM, VQA, and T2I), we empirically demonstrate that DSG addresses the challenges noted above. Finally, we present DSG-1k, an open-sourced evaluation benchmark that includes 1,060 prompts, covering a wide range of fine-grained semantic categories with a balanced distribution. We release the DSG-1k prompts and the corresponding DSG questions.Comment: Project website: https://google.github.io/ds

One Embedder, Any Task: Instruction-Finetuned Text Embeddings

We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate text embeddings tailored to different downstream tasks and domains, without any further training. We first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive loss. We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training), ranging from classification and information retrieval to semantic textual similarity and text generation evaluation. INSTRUCTOR, while having an order of magnitude fewer parameters than the previous best model, achieves state-of-the-art performance, with an average improvement of 3.4% compared to the previous best results on the 70 diverse datasets. Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets. Our model, code, and data are available at https://instructor-embedding.github.io.Comment: Accepted in ACL2023 Finding