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Zehao Sun

Publications and source records attributed to Zehao Sun.

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A monolithic fabrication platform for intrinsically stretchable polymer transistors and complementary circuits

Soft, stretchable organic field-effect transistors (OFETs) can provide powerful on-skin signal conditioning, but current fabrication methods are often material-specific: each new polymer semiconductor (PSC) requires a tailored process. The challenge is even greater for complementary OFET circuits, where two PSCs must be patterned sequentially, which often leads to device degradation. Here, we introduce a universal, monolithic photolithography process that enables high-yield, high-resolution stretchable complementary OFETs and circuits. This approach is enabled by a process-design framework that includes (i) a direct, photopatternable, solvent-resistant, crosslinked dielectric/semiconductor interface, (ii) broadly applicable crosslinked PSC blends that preserve high mobility, and (iii) a patterning strategy that provides simultaneous etch masking and encapsulation. Using this platform, we achieve record integration density for stretchable OTFTs (55,000 cm^-2), channel lengths down to 2 um, and low-voltage operation at 5 V. We demonstrate photopatterning across multiple PSC types and realize complementary circuits, including 3 kHz stretchable ring oscillators, the first to exceed 1 kHz and representing more than a 60-fold increase in stage switching speed over the state of the art. Finally, we demonstrate the first stretchable complementary OTFT neuron circuit, where the output frequency is modulated by the input current to mimic neuronal signal processing. This scalable approach can be readily extended to diverse high-performance stretchable materials, accelerating the development and manufacturing of skin-like electronics.

eess.SY

SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions

Recent advancements in diffusion models have positioned them at the forefront of image generation. Despite their superior performance, diffusion models are not without drawbacks; they are characterized by complex architectures and substantial computational demands, resulting in significant latency due to their iterative sampling process. To mitigate these limitations, we introduce a dual approach involving model miniaturization and a reduction in sampling steps, aimed at significantly decreasing model latency. Our methodology leverages knowledge distillation to streamline the U-Net and image decoder architectures, and introduces an innovative one-step DM training technique that utilizes feature matching and score distillation. We present two models, SDXS-512 and SDXS-1024, achieving inference speeds of approximately 100 FPS (30x faster than SD v1.5) and 30 FPS (60x faster than SDXL) on a single GPU, respectively. Moreover, our training approach offers promising applications in image-conditioned control, facilitating efficient image-to-image translation.

cs.CV