Researchers in the Murmann Mixed Signal Group have developed a pipelined chip architecture with inverted residual and linear bottlenecks-based networks for energy efficient Machine Learning inference on edge devices.
The Fan Lab at Stanford University has developed an ultra-fast, physics-augmented, deep learning enhanced surrogate field solver for high-speed electromagnetic simulation and optimization. Denoising WaveY-Net uses a two-stage approach to target different field error sources.
Researchers at Stanford have developed a technique that can rapidly and sequentially separate multiple sets of III-V solar cell thin films grown as a stack on one III-V wafer.
Stanford Nanoscale and Quantum Photonics Lab researchers developed a passive, magnet free, integrated on-chip laser stabilization and isolation device. Lasers need a way to prevent the light they emit from reflecting into the laser and destabilizing it.
We present a revolutionary advancement in ferroelectric materials that is set to redefine the landscape of embedded memories and semiconductor technologies.
Researchers at Stanford University have developed Schottky contacts for aluminum nitride-based microelectronic devices. The contacts enable reliable device operation at up to 600 ºC, opening up opportunities for high temperature microelectronic performance.
Researchers at Stanford are designing a class of "2.5-dimensional" microwave cavities that enables coupling/interaction between microwave photons and solid-state quantum spins in a way which is strong compared to loss.
Researchers at Stanford have developed a new synthetic strategy for self-assembling layered heterostructures into large single crystals and films useful in microelectronics.
Scientists in the Zhenan Bao Research Group at Stanford developed a process for direct photo-patterning of electronic polymers that improves device density of elastic circuits over 100x.
Stanford researchers have developed new Fast Quick Error Detection (Fast QED) tests that are four orders of magnitude faster than standard QED tests while also preserving quick error detection properties.
The Foundational QED embodies a set of source code files for performing the basic EDDI, CFCSS, and CFTSS QED transformations for creating tests with extremely short error detection latencies and high error detection coverage.