This paper analyzes an effect of inter-wire skew on signal integrity in a multi-channel system and proposes circuit techniques to enhance signal integrity by using a spatially correlated coded signaling (SCCS). Through the in-depth analysis, the paper investigates a relation between skew and insertion loss (IL), and highlights an importance of wide-range inter-wire de-skewing (IWD). The proposed phase interpolator (PI)-based IWD technique adjusts the transition edges of receiver inputs to minimize skew caused by mismatch components, which increases timing jitter. The transceiver employs pseudo-differential signaling to verify the effect of the IWD in SCCS. The transmitter integrates a feed-forward equalizer (FFE) with a latch-based re-timer and a PI for each channel. Additionally, a 3-tap FFE at the transmitter and a continuous-time linear equalizer (CTLE) at the receiver are incorporated to compensate for channel ISI. Fabricated in 40-nm CMOS technology, the proposed 8-channel transceiver achieves an aggregate throughput of 112 Gb/s with a bit error rate (BER) below$10^{\mathbf {-12}}$. The transceiver occupies 0.382 mm2while achieving energy efficiencies of 0.77 pJ/bit/ch for the transmitter and 1.45 pJ/bit/ch for the receiver.

We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars. Due to the wide variety of human identities, poses, and stochastic details, the generation of 3D human meshes has been a challenging problem. To address this, we decompose the problem into 2D normal map generation and normal map-based 3D reconstruction. Specifically, we first simultaneously generate realistic normal maps for the front and backside of a clothed human, dubbed dual normal maps, using a pose-conditional diffusion model. For 3D reconstruction, we "carve" the prior SMPL-X mesh to a detailed 3D mesh according to the normal maps through mesh optimization. To further enhance the high-frequency details, we present a diffusion resampling scheme on both body and facial regions, thus encouraging the generation of realistic digital avatars. We also seamlessly incorporate a recent text-to-image diffusion model to support text-based human identity control. Our method, namely, Chupa, is capable of generating realistic 3D clothed humans with better perceptual quality and identity variety.