GlobalFoundries (also known as GF) is an American semiconductor foundry headquartered in Santa Clara, California, United States. GlobalFoundries was created by the divestiture of the manufacturing arm of Advanced Micro Devices (AMD). The Emirate of Abu Dhabi is the owner of the company through its subsidiary Advanced Technology Investment Company (ATIC). The firm manufactures integrated circuits in high volume mostly for semiconductor companies such as AMD, Broadcom, Qualcomm, and STMicroelectronics. It has five 200 mm wafer fabrication plants in Singapore, one 300 mm plant each in Germany and Singapore, and three plants in the United States: one 200 mm plant in Vermont (where it is the largest private employer) and two 300 mm plants in New York.GlobalFoundries plans to become a publicly traded company in 2022.
In this work, we propose a novel differential photonic static random access memory (pSRAM) bitcell design using fabrication-friendly photonic components. The proposed pSRAM overcomes the key limitations of traditional electrical SRAMs, which struggle with speed and power efficiency due to increasing bitline/wordline capacitance and interconnect resistance associated with long electrical wires as technology scales. By utilizing cross-coupled micro-ring resonators and differential photodiode structures, along with optical waveguides instead of traditional wordlines and bitlines, our pSRAM exhibits high-speed, and energy-efficient performance. The pSRAM bitcell demonstrates a read/write speed of 40 GHz, with a switching (static) energy consumption of approximately 0.6 pJ (0.03 pJ) per bit and a footprint of 330x290 um^2 using the GlobalFoundries 45SPCLO process node. These bitcells can be arranged into a 2D memory array, enabling large-scale, on-chip photonic memory subsystems ideal for high-speed memory, data processing and computing applications.
Understanding and monitoring phase transformations in tungsten thin films is crucial for advancing applications in spintronics, including MRAM technology. This study investigates spectroscopic ellipsometry as a non-destructive method for identifying and distinguishing between the tungsten phases beta-tungsten ( β -W) and alpha-tungsten (α-W) in thin films. In this study, β -W thin films of, ranging from 15 to 150 nm in thickness, were deposited on silicon substrates using DC magnetron sputtering. Phase transition to α-W was then induced in selected samples. The films were characterized using atomic force microscopy (AFM), scanning electron microscopy (SEM), grazing-incidence x-ray diffraction (GIXRD), x-ray reflectivity (XRR), and four-point probe resistivity. Optical properties were subsequently measured using a spectroscopic ellipsometer, and the data were used to build and fit B-Spline models in CompleteEASE 6 software. The optical parameter data from the tested samples were then fitted successfully for both tungsten phases. The data analysis has shown distinct refractive index patterns for the α -W and β -W phases in films with thicknesses of 15, 30, and 60 nm. This work demonstrates that spectroscopic ellipsometry can effectively be used as an additional in situ tool, complementing other characterization techniques, to detect and monitor phase transformations in tungsten thin films.
This work investigates the impact of epitaxial layer (Epi) thickness on the holding voltage (Vh) scalability of bidirectional silicon-controlled rectifiers (BiSCRs) in 130 nm BCD technology. Experimental results show that increasing the base lengths (Lnb/Lpb) of parasitic BJTs leads to a linear increase in Vh under a thicker epitaxial process (1.5×), while having a negligible impact in a thinner epitaxial process (1×). Technology computer-aided design (TCAD) simulations attribute this disparity to Epi dependent current gain variations in the parasitic NPN and PNP BJTs: thinner Epi strengthens NPN gain, dominating conduction current and suppressing Vh control via lateral base-length adjustments, while thicker Epi weakens NPN gain, enabling effective Vh tuning. These findings demonstrate that Epi thickness critically determines the efficacy of geometric design strategies for BiSCR latch-up immunity, guiding optimized ESD protection in holding-voltage sensitive applications.
Precise prediction of processing time (PT) and machine availability is critical for optimizing fabrication throughput. This paper presents a generalized, attention-based deep neural network designed to capture non-linear tool dynamics and high-dimensional categorical dependencies. Unlike rigid statistical baselines, our framework adapts to multiple prediction targets, including equipment-level process completion and granular chamber-level availability. Deployed in multiple GlobalFoundries semiconductor fabrication plants, the model demonstrates a 50% to 80% reduction in mean absolute error (MAE) compared to historical averages. Production results confirm a 2% to 3% increase in moves per productive hour and a 3% to 7% sustained increase in chamber utilization across a majority of the tools that we tested with. These results validate the strength of machine learning methods in enabling advanced parallel loading and just-in-time dispatching strategies.
Semiconductor Fabs consume substantial amounts of energy, mainly in the form of electric power, to operate manufacturing equipment and to rigorously control the environment in the Fab. Heating and cooling of the cleanroom and the equipment is a significant part of the energy demand. We have found opportunities to reduce the demand by carefully calibrated changes of two systems, the control of relative humidity (RH) in the cleanroom and the way in which we supply cooling water to the equipment. Varying these conditions can lower energy consumption and cost, but it can also affect the production process in a number of ways. To avoid adverse effects we established a list of potential issues and a plan to prevent any deviations. We then varied RH in a controlled fashion and verified that production can continue without issues. A similar approach was used to make a change to the cooling water supply. By equalizing the temperature between two separate systems used across tool platforms and then connecting these systems we gain efficiency, reduce complexity and save energy. As with the humidity change the challenge is to ensure there is no impact across all equipment affected by the change. We achieved a significant reduction of energy consumption through these changes.
