Transfer print techniques for heterogeneous integration of photonic components

The essential functionality of photonic and electronic devices is contained in thin surface layers leaving the substrate often to play primarily a mechanical role. Layer transfer of optimised devices or materials and their heterogeneous integration is thus a very attractive strategy to realise high performance, low-cost circuits for a wide variety of new applications. Additionally, new device configurations can be achieved that could not otherwise be realised. A range of layer transfer methods have been developed over the years including epitaxial lift-off and wafer bonding with substrate removal. Recently, a new technique called transfer printing has been introduced which allows manipulation of small and thin materials along with devices on a massively parallel scale with micron scale placement accuracies to a wide choice of substrates such as silicon, glass, ceramic, metal and polymer. Thus, the co-integration of electronics with photonic devices made from compound semiconductors, silicon, polymer and new 2D materials is now achievable in a practical and scalable method. This is leading to exciting possibilities in microassembly. We review some of the recent developments in layer transfer and particularly the use of the transfer print technology for enabling active photonic devices on rigid and flexible foreign substrates

PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts. In response to this vital need, we introduce PromptBench, a robustness benchmark designed to measure LLMs' resilience to adversarial prompts. This study uses a plethora of adversarial textual attacks targeting prompts across multiple levels: character, word, sentence, and semantic. These prompts are then employed in diverse tasks, such as sentiment analysis, natural language inference, reading comprehension, machine translation, and math problem-solving. Our study generates 4,032 adversarial prompts, meticulously evaluated over 8 tasks and 13 datasets, with 567,084 test samples in total. Our findings demonstrate that contemporary LLMs are vulnerable to adversarial prompts. Furthermore, we present comprehensive analysis to understand the mystery behind prompt robustness and its transferability. We then offer insightful robustness analysis and pragmatic recommendations for prompt composition, beneficial to both researchers and everyday users. We make our code, prompts, and methodologies to generate adversarial prompts publicly accessible, thereby enabling and encouraging collaborative exploration in this pivotal field: https://github.com/microsoft/promptbench.Comment: Technical report; 23 pages; code is at: https://github.com/microsoft/promptbenc

MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use

Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. Recently, many studies have focused on the tool utilization ability of LLMs. They primarily investigated how LLMs effectively collaborate with given specific tools. However, in scenarios where LLMs serve as intelligent agents, as seen in applications like AutoGPT and MetaGPT, LLMs are expected to engage in intricate decision-making processes that involve deciding whether to employ a tool and selecting the most suitable tool(s) from a collection of available tools to fulfill user requests. Therefore, in this paper, we introduce MetaTool, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools. Specifically, we create a dataset called ToolE within the benchmark. This dataset contains various types of user queries in the form of prompts that trigger LLMs to use tools, including both single-tool and multi-tool scenarios. Subsequently, we set the tasks for both tool usage awareness and tool selection. We define four subtasks from different perspectives in tool selection, including tool selection with similar choices, tool selection in specific scenarios, tool selection with possible reliability issues, and multi-tool selection. We conduct experiments involving nine popular LLMs and find that the majority of them still struggle to effectively select tools, highlighting the existing gaps between LLMs and genuine intelligent agents. However, through the error analysis, we found there is still significant room for improvement. Finally, we conclude with insights for tool developers that follow ChatGPT to provide detailed descriptions that can enhance the tool selection performance of LLMs

Fano-Resonance Photonic Crystal Membrane Reflectors at Mid- and Far-Infrared

We report here single-layer ultracompact Fano-resonance photonic crystal membrane reflectors (MRs) at mid-infrared (IR) and far-IR (FIR) bands, based on single layer crystalline Si membranes. High-performance reflectors were designed for surface-normal incidence illumination with center operation wavelengths up to the 75-mu m FIR spectral band. Large-area patterned MRs were also fabricated and transferred onto glass substrates based on membrane transfer processes. Close to 100% reflection was obtained at the similar to 76-mu m spectral band, with a single-layer Si membrane thickness of 18 mu m. Such Fano-resonance-based membranes reflectors offer great opportunities for high-performance ultracompact dielectric reflectors at IR and THz regions