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1. Multi-Objective Design Automation for Microfluidic Capture Chips

   https://doi.org/10.1109/TNB.2022.3212625  

   Chen, Lisa ; Grover, William H. ; Sridharan, Manu ; Brisk, Philip ( July 2023 , IEEE Transactions on NanoBioscience)
2. An Approximate Communication Framework for Network-on-Chips

   https://doi.org/10.1109/TPDS.2020.2968068  

   Chen, Yuechen ; Louri, Ahmed ( June 2020 , IEEE Transactions on Parallel and Distributed Systems)
3. Building Blood Vessel Chips with Enhanced Physiological Relevance

   https://doi.org/10.1002/admt.202201778  

   Mu, Xuan ; Gerhard‐Herman, Marie Denise ; Zhang, Yu Shrike ( February 2023 , Advanced Materials Technologies)

   Blood vessel chips are bioengineered microdevices, consisting of biomaterials, human cells, and microstructures, which recapitulate essential vascular structure and physiology and allow a well‐controlled microenvironment and spatial‐temporal readouts. Blood vessel chips afford promising opportunities to understand molecular and cellular mechanisms underlying a range of vascular diseases. The physiological relevance is key to these blood vessel chips that rely on bioinspired strategies and bioengineering approaches to translate vascular physiology into artificial units. Here, several critical aspects of vascular physiology are discussed, including morphology, material composition, mechanical properties, flow dynamics, and mass transport, which provide essential guidelines and a valuable source of bioinspiration for the rational design of blood vessel chips. The state‐of‐art blood vessel chips are also reviewed that exhibit important physiological features of the vessel and reveal crucial insights into the biological processes and disease pathogenesis, including rare diseases, with notable implications for drug screening and clinical trials. It is envisioned that the advances in biomaterials, biofabrication, and stem cells improve the physiological relevance of blood vessel chips, which, along with the close collaborations between clinicians and bioengineers, enable their widespread utility.

    

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4. DRAM Retention Behavior with Accelerated Aging in Commercial Chips

   https://doi.org/10.3390/app12094332  

   Bepary, Md Kawser ; Talukder, Bashir Mohammad ; Rahman, Md Tauhidur ( May 2022 , Applied Sciences)

   The cells in dynamic random access memory (DRAM) degrade over time as a result of aging, leading to poor performance and potential security vulnerabilities. With a globalized horizontal supply chain, aged counterfeit DRAMs could end up on the market, posing a significant threat if employed in critical infrastructure. In this work, we look at the retention behavior of commercial DRAM chips from real-time silicon measurements and investigate how the reliability of DRAM cells degrade with accelerated aging. We analyze the retention-based errors at three different aging points to observe the design-induced variations, analyze the pattern dependency, and explore the impacts of accelerated aging for multiple DRAM vendors. We also investigate the DRAM chips’ statistical distribution to attribute the vital wear-out effects present in DRAM. We see a continuous increase in retention error as DRAM chips age and therefore infer that the aged retention signatures can be used to differentiate recycled DRAM chips in the supply chain. We also discuss the roles of device signature in DRAM aging and aging-related security implication on DRAM row-hammer error. 

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5. Approximate Network-on-Chips with Application to Image Classification

   https://doi.org/10.1109/NAS55553.2022.9925540  

   Chen, Yuechen ; Louri, Ahmed ; Liu, Shanshan ; Lombardi, Fabrizio ( October 2022 , IEEE International Conference on Networking, Architecture, and Storage (NAS))