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Today's analog/mixed-signal (AMS) integrated circuit (IC) designs demand substantial manual intervention. The advent of multimodal large language models (MLLMs) has unveiled significant potential across various fields, suggesting their applicability in streamlining large-scale AMS IC design as well. A bottleneck in employing MLLMs for automatic AMS circuit generation is the absence of a comprehensive dataset delineating the schematic-netlist relationship. We therefore design an automatic technique for converting schematics into netlists, and create dataset AMSNet, encompassing transistor-level schematics and corresponding SPICE format netlists. With a growing size, AMSNet can significantly facilitate exploration of MLLM applications in AMS circuit design. We have made an initial set of netlists public, and will make both our netlist generation tool and the full dataset available upon publishing of this paper.

Multimodal large language models (MLLMs) have demonstrated remarkable problem-solving capabilities in various vision fields (e.g., generic object recognition and grounding) based on strong visual semantic representation and language reasoning ability. However, whether MLLMs are sensitive to subtle visual spoof/forged clues and how they perform in the domain of face attack detection (e.g., face spoofing and forgery detection) is still unexplored. In this paper, we introduce a new benchmark, namely SHIELD, to evaluate the ability of MLLMs on face spoofing and forgery detection. Specifically, we design true/false and multiple-choice questions to evaluate multimodal face data in these two face security tasks. For the face anti-spoofing task, we evaluate three different modalities (i.e., RGB, infrared, depth) under four types of presentation attacks (i.e., print attack, replay attack, rigid mask, paper mask). For the face forgery detection task, we evaluate GAN-based and diffusion-based data with both visual and acoustic modalities. Each question is subjected to both zero-shot and few-shot tests under standard and chain of thought (COT) settings. The results indicate that MLLMs hold substantial potential in the face security domain, offering advantages over traditional specific models in terms of interpretability, multimodal flexible reasoning, and joint face spoof and forgery detection. Additionally, we develop a novel Multi-Attribute Chain of Thought (MA-COT) paradigm for describing and judging various task-specific and task-irrelevant attributes of face images, which provides rich task-related knowledge for subtle spoof/forged clue mining. Extensive experiments in separate face anti-spoofing, separate face forgery detection, and joint detection tasks demonstrate the effectiveness of the proposed MA-COT. The project is available at https$:$//github.com/laiyingxin2/SHIELD