Chip Industry Technical Paper Roundup: May 19


New technical papers recently added to Semiconductor Engineering’s library: Technical Paper Research Organizations Micro-Transfer Printing on Silicon Photonics: Tutorial, Recent Progress and Outlook 🔗 Ghent U., imec Challenges and prospects of 2D electronics for future monolithic CFETs 🔗 SKKU, Hanyang U. et al. A Device-Physics-Informed Artific... » read more

Research Bits: May 11


Non-destructive terahertz inspection Researchers from Adelaide University, Virginia Diodes, the Hasso Plattner Institute, and the University of Potsdam used terahertz waves to observe electrical activity inside fully packaged semiconductor devices as they are operating. The technique relies on an ultra-sensitive detection system using a specialized homodyne quadrature receiver, which can pi... » read more

Chip Industry Technical Paper Roundup: May 11


New technical papers recently added to Semiconductor Engineering’s library: Technical Paper Research Organizations Source-position-dependent transmission cross coefficient formula including polarization and mask three-dimensional effects in High NA EUV🔗 Science Tokyo Performance and Energy Benefits of MRDIMMs 🔗 Barcelona Supercomputing Center, UPC, ... » read more

Chip Industry Technical Paper Roundup: May 5


New technical papers recently added to Semiconductor Engineering’s library: Technical Paper Research Organizations Rethinking Compute Substrates for 3D-Stacked Near-Memory LLM Decoding: Microarchitecture-Scheduling Co-Design 🔗 Univ. of Edinburgh, Peking Univ., Cambridge, CAS, HKUST In-SoIC ESD Protection for Chiplet-Based 3D Microsystems: Future Research Direct... » read more

Research Bits: May 5


AI power prediction Researchers from MIT and the MIT-IBM Watson AI Lab developed a prediction tool that can quickly tell data center operators how much power will be consumed by running a particular AI workload on a certain processor or AI accelerator chip. It can be applied to a wide range of hardware configurations. The lightweight estimation model captures the power usage pattern of a GP... » read more

Research Bits: Apr. 28


Parchment papertronics Researchers from Binghamton University used commercial parchment paper, commonly used in baking, along with a standard carbon dioxide laser and water-based conductive ink to create disposable, single-use electronic circuits. The laser selectively removes the paper's thin silicone coating in specific patterns, exposing the water-absorbing cellulose fibers underneath. T... » read more

Chip Industry Technical Paper Roundup: Apr. 21


New technical papers recently added to Semiconductor Engineering’s library: Technical Paper Research Organizations Neural Computers 🔗 Meta AI, KAUST Characterizing tip-sample interaction dynamics on EUV nanostructures using AFM with a high-aspect ratio tip 🔗 Purdue University, Intel, Bruker  Photonic chip packaging for extreme environments ὑ... » read more

Research Bits: Apr. 21


Compute-in-memory state space models Researchers from the University of Michigan mapped complex state space models directly onto a compute-in-memory architecture in an example of hardware-software co-design for edge AI. "Compute-in-memory systems offer very high energy efficiency and throughput, but they are rigid and not optimal for convolution and transformer networks. In this study, we s... » read more

Research Bits: Apr. 14


Authentication for edge devices Researchers from the University of Hong Kong, Tsinghua University, and the Southern University of Science and Technology designed a privacy-preserving system for edge devices that combines physically unclonable functions and compute-in-memory. The Co-Located Authentication and Processing (CLAP) system integrates authentication and processing functions within ... » read more

Chip Industry Technical Paper Roundup: Apr. 14


New technical papers recently added to Semiconductor Engineering’s library: Technical Paper Research Organizations Device/circuit simulations of silicon spin qubits based on a gate-all-around transistor 🔗 Teikyo University, RIKEN Causal AI For AMS Circuit Design: Interpretable Parameter Effects Analysis 🔗 University of Florida Reliability of Wide B... » read more

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