OmniLayout: A Schematic-Coupled Multimodal Benchmark for Constraint-Aware Geometric Reasoning in PCB Layout

Recent large language models (LLMs) have demonstrated remarkable progress in 3D spatial reasoning, spatial grounding, and fine-grained geometric understanding. However, their ability to reason about densely packed object placement under strict spatial and functional constraints remains largely unexplored, despite being a fundamental challenge in practical electronic design automation (EDA) workflows. To bridge this gap, we introduce OmniLayout, the first benchmark designed to evaluate LLMs on printed-circuit-board (PCB) layout placement reasoning under real-world geometric, routing, and connectivity constraints. OmniLayout contains 1,681 industrial-grade schematic-coupled PCB layouts and includes four tasks: (1) geometric reasoning for IC physical placement, with 77.24K placement instances constrained within PCB board boundaries; (2) routability-aware placement reasoning, generating physically valid component placements; (3) electrical functionality, preserving schematic-specified connectivity and electronic functional correctness; and (4) tool-augmented agentic reasoning for invoking external tools to accomplish tasks (1)-(3). Our results reveal substantial limitations of current LLMs in PCB layout placement, including weak geometric reasoning, poor routability optimization, and inconsistent preservation of electrical functionality.

OmniSch: A Multimodal PCB Schematic Benchmark For Structured Diagram Visual Reasoning

Recent large multimodal models (LMMs) have made rapid progress in visual grounding, document understanding, and diagram reasoning tasks. However, their ability to convert Printed Circuit Board (PCB) schematic diagrams into machine-readable spatially weighted netlist graphs, jointly capturing component attributes, connectivity, and geometry, remains largely underexplored, despite such graph representations are the backbone of practical electronic design automation (EDA) workflows. To bridge this gap, we introduce OmniSch, the first comprehensive benchmark designed to assess LMMs on schematic understanding and spatial netlist graph construction. OmniSch contains 1,854 real-world schematic diagrams and includes four tasks: (1) visual grounding for schematic entities, with 109.9K grounded instances aligning 423.4K diagram semantic labels to their visual regions; (2) diagram-to-graph reasoning, understanding topological relationship among diagram elements; (3) geometric reasoning, constructing layout-dependent weights for each connection; and (4) tool-augmented agentic reasoning for visual search, invoking external tools to accomplish (1)-(3). Our results reveal substantial gaps of current LMMs in interpreting schematic engineering artifacts, including unreliable fine-grained grounding, brittle layout-to-graph parsing, inconsistent global connectivity reasoning and inefficient visual exploration.

MmBack: Clock-free Multi-Sensor Backscatter with Synchronous Acquisition and Multiplexing

Backscatter tags provide a low-power solution for sensor applications, yet many real-world scenarios require multiple sensors-often of different types-for complex sensing tasks. However, existing designs support only a single sensor per tag, increasing spatial overhead. State-of-the-art approaches to multiplexing multiple sensor streams on a single tag rely on onboard clocks or multiple modulation chains, which add cost, enlarge form factor, and remain prone to timing drift-disrupting synchronization across sensors. We present mmBack, a low-power, clock-free backscatter tag that enables synchronous multi-sensor data acquisition and multiplexing over a single modulation chain. mmBack synchronizes sensor inputs in parallel using a shared reference signal extracted from ambient RF excitation, eliminating the need for an onboard timing source. To efficiently multiplex sensor data, mmBack designs a voltage-division scheme to multiplex multiple sensor inputs as backscatter frequency shifts through a single oscillator and RF switch. At the receiver, mmBack develops a frequency tracking algorithm and a finite-state machine for accurate demultiplexing. mmBack's ASIC design consumes 25.56uW, while its prototype supports 5 concurrent sensor streams with bandwidths of up to 5kHz and 3 concurrent sensor streams with bandwidth of up to 18kHz. Evaluation shows that mmBack achieves an average SNR surpassing 15dB in signal reconstruction.