[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125017-en":3,"doc-seo-125017-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125017,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning-Assisted Thermoelectric Cooling for On-Demand Multi-Hotspot Thermal Management - Paper","Thermoelectric coolers (TECs) enable direct local hotspot cooling, yet real-world deployment faces strong trade-offs between spatial cooling, heating, and power consumption. Extensive simulation-based optimization is impractical for systems with multiple hotspots that evolve across space and time. This work introduces a machine learning-assisted optimization method that predicts temperature and power using a CNN trained with Inception and multi-task learning, then searches TEC assignments via backtracking to reach global optimum control. The approach reduces peak temperature by 52.4% while delivering solutions in about 1.64 seconds, far faster than FEM-based workflows.","Machine Learning-Assisted Thermoelectric Cooling for On-Demand MultiHotspot Thermal Management  \nJiajian Luo 1 and Jaeho Lee 1,a)  \n1Department of Mechanical and Aerospace Engineering, University of California Irvine, Irvine, CA, 92697, USA  \nThermoelectric coolers (TECs) offer a promising solution for direct cooling of local hotspots and active thermal management in advanced electronic systems. However, TECs present significant trade-offs among spatial cooling, heating and power consumption. The optimization of TECs requires extensive simulations, which are impractical for managing actual systems with multiple hotspots under spatial and temporal variations. In this study, we present a novel machine learning-assisted optimization algorithm for thermoelectric coolers that can achieve global optimal temperature by individually controlling TEC units based on real-time multi-hotspot conditions across the entire domain. We train a convolutional neural network (CNN) with a combination of the Inception module and multi-task learning (MTL) approach to comprehend the coupled thermal-electrical physics underlying the system and attain accurate predictions for both temperature and power consumption with and without TECs. Due to the intricate interaction among passive thermal gradient, Peltier effect and Joule effect, a local optimal TEC control experiences spatial temperature trade-off which may not lead to a global optimal solution. To address this issue, we develop a backtracking-based optimization algorithm using the machine learning model to iterate all possible TEC assignments for attaining global optimal solutions. For any m × n matrix with NHS hotspots (n, m ≤ 10, 1 ≤ NHS ≤ 20), our algorithm is capable of providing 52.4% peak temperature reduction and its corresponding TEC array control within an average of 1.64 seconds while iterating through tens of temperature predictions behind-the-scenes. This represents a speed increase of over three orders of magnitude compared to traditional FEM strategies which take approximately 27 minutes.  \nI. INTRODUCTION  \nDespite great advancements in semiconductor technology beyond the sub-3nm node1, most thermal management techniques nowadays are limited to the macroscale operation. The trend towards device miniaturization and the rapid emergence of System-on-Chip (SoC) inevitably complicate the thermal behavior within microelectronic devices2,3 . Specifically, multiple on-chip hotspots exhibit spatial and temporal changes due to workload variations, environmental fluctuations, device defects and aging, which can occur among modules4,5, cores (processors)6,7and transistors8. The complexity of the hotspot behavior presents unprecedented challenges for conventional thermal management methods which only rely on uniform control, necessitating a more efficient, sophisticated, and intelligent approach capable of on-demand thermal management to ensure optimal functionality and longevity of microelectronic devices9.  \nAmong various active cooling techniques, thermoelectric coolers (TECs) offer distinctive local cooling capability as well as several other advantages10–12, making them a promising solution to hotspot thermal management. In recent years, there have been emerging designs utilizing single TECs13–16 and TEC arrays17–19 for on-chip hotspot cooling in microelectronic devices. Revolutionary materials, including nanostructured Si20–22, self-hygroscopic hydrogel23 and flexible inorganics24,25,  \na) Correspondence author. Email: [jaeholee@uci.edu](jaeholee@uci.edu).  \nare extensively studied to improve TEC cooling performance. However, TEC cooling exhibits significant trade-offs in spatial temperature and power consumption, and its performance relies on multiple variables including TEC voltages and hotspot conditions12,26 . The high non-linearity in TEC behavior requires multiple solutions for optimization, which brings expensive computational cost to conventional finite element method (FEM) simulati","cbCaihjxlNjNqnjC","https://ap.wps.com/l/cbCaihjxlNjNqnjC","pdf",8421756,1,18,"English","en",105,"# Introduction\n## Thermal challenges in advanced microelectronics\n## Trade-offs and limitations of thermoelectric coolers\n## Motivation for machine learning in TEC optimization\n# Proposed ML-assisted global optimization method\n## CNN model for coupled thermal-electrical prediction\n## Backtracking-based assignment search for global optimum\n## Performance and speedup versus FEM","[{\"question\":\"What problem does the study address in thermoelectric cooler (TEC) control?\",\"answer\":\"TEC optimization must balance spatial temperature effects and power consumption, but traditional simulation and uniform control methods struggle when multiple hotspots change across space and time.\"},{\"question\":\"How does the proposed method predict temperature and power consumption?\",\"answer\":\"It trains a convolutional neural network using an Inception module and a multi-task learning approach to capture coupled thermal-electrical physics and output both temperature and power with and without TECs.\"},{\"question\":\"Why is backtracking used in the optimization stage?\",\"answer\":\"Because local optimal TEC control can cause spatial temperature redistribution that prevents reaching a global optimum; backtracking iterates through TEC assignments to find the global best solution.\"}]","Machine Learning-Assisted Thermoelectric Cooling for On-Demand Multi-Hotspot Thermal Management - Paper | PDF",1785896171,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-assisted-thermoelectric-cooling-for-on-demand-multi-hotspot-thermal-management-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-assisted-thermoelectric-cooling-for-on-demand-multi-hotspot-thermal-management-paper/125017/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in thermoelectric cooler (TEC) control?","Question",{"text":75,"@type":76},"TEC optimization must balance spatial temperature effects and power consumption, but traditional simulation and uniform control methods struggle when multiple hotspots change across space and time.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method predict temperature and power consumption?",{"text":80,"@type":76},"It trains a convolutional neural network using an Inception module and a multi-task learning approach to capture coupled thermal-electrical physics and output both temperature and power with and without TECs.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is backtracking used in the optimization stage?",{"text":84,"@type":76},"Because local optimal TEC control can cause spatial temperature redistribution that prevents reaching a global optimum; 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