Effect of temperature and humidity conditioning on mold compound/copper interfacial fracture and the associated cohesive zone models

Microelectronic packages consist of multilayered structures made of dissimilar materials. Interfacial delamination is a common failure mechanism present in microelectronic packages due to the mismatch in the coefficient of thermal expansion (CTE) between different materials. Epoxy Mold Compound (EMC) on a copper leadframe is a common interface found in microelectronic packages. This work analyzes delamination at an EMC/Copper interface and how the interfacial fracture energy is affected by temperature and humidity conditioning. This work employs delamination experiments to determine how the temperature and humidity conditioning will influence the cohesive zone models (CZM). Starting with an EMC/Copper sample with a starter crack, this work uses a Double Cantilever Beam (DCB) test to obtain the critical load for crack propagation at various crack lengths. Using the experimental data in combination with numerical models, this work obtains the interfacial strain energy release rate (SERR) using fracture mechanics techniques. EMC/Copper samples are thermally aged and humidity conditioned for different conditions based on accelerated stress tests for packaged integrated circuits used by industry. DCB tests are run on these samples after conditioning and the SERR of the interface is obtained. Using the SERR, cohesive zone models are developed for as-received, thermally aged and moisture-conditioned samples. Thermal aging has shown to reduce interfacial adhesion at longer exposure times. Humidity conditioning has shown to reduce interfacial adhesion as well. Modified cohesive zone models reflect such changes in the interfacial fracture energy. The effect of a viscoelastic material model for the EMC is also incorporated in the interfacial fracture energy calculation. The modified CZM parameters, as obtained in this work, can be used to study the reliability of SOIC and other microelectronic packages under thermal aging and humidity conditioning.M.S

Real-Time Selective Monitoring of Exposure Controlled Projection Lithography

Exposure Controlled Projection Lithography (ECPL) is a stereolithographic process in which incident radiation, patterned by a dynamic mask, passes through a transparent substrate to cure photopolymer which grows progressively from the substrate surface. We present here a novel method of capturing useful information about the curing process from a simple, inexpensive, real-time monitoring system based on interferometry. This approach can be used to provide feedback control to the ECPL process, thus making the process more robust and increasing system accuracy. The results obtained from this monitoring system provide a means to better visualize and understand the various phenomena occurring during the photopolymerization of transparent photopolymers. In order to lessen the measurement error, caused by internal diffraction within the substrate, the interferometry system has been designed such that the laser light used can be selectively targeted. This selective monitoring approach is experimentally validated to measure the height and profile of the cured part in real-time.Mechanical Engineerin

Process Planning Method for Exposure Controlled Projection Lithography

An Exposure Controlled Projection Lithography (ECPL) process with the ability to cure lens shaped structures on transparent substrates is presented. This process can be used to create microlenses and micro fluidic channels on flat or curved substrates. Incident radiation, patterned by a dynamic mask, passes through a transparent substrate to cure photopolymer resin that grows progressively from the substrate surface. A resin response model which incorporates the effects of oxygen inhibition during photopolymerization is used to formulate a process planning method for ECPL. This process planning method is validated for fabricating lens shaped structure on flat transparent substrates using the ECPL system.Mechanical Engineerin

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc