T. Nogami, H. Huang, H. Shobha, R. Patlolla, J. Kelly, C. Penny, C. Hu, D. Sil, S. DeVries, J. Lee, S. Nguyen, L. Jiang, J. Demarest, J. Li, G. Lian, M. Ali, P. Bhosale, N. Lanzillo, K. Motoyama, S. Lian, T. Standaert, G. Bonilla, D. Edelstein, B. Haran
{"title":"Technology challenges and enablers to extend Cu metallization to beyond 7 nm node","authors":"T. Nogami, H. Huang, H. Shobha, R. Patlolla, J. Kelly, C. Penny, C. Hu, D. Sil, S. DeVries, J. Lee, S. Nguyen, L. Jiang, J. Demarest, J. Li, G. Lian, M. Ali, P. Bhosale, N. Lanzillo, K. Motoyama, S. Lian, T. Standaert, G. Bonilla, D. Edelstein, B. Haran","doi":"10.23919/VLSIT.2019.8776573","DOIUrl":"https://doi.org/10.23919/VLSIT.2019.8776573","url":null,"abstract":"Electromigration (EM) and TDDB reliability of Cu interconnects with a barrier/wetting layer as thin as 2 nm employing a PVD-reflowed through-Co self-forming barrier (tCoSFB) is demonstrated to meet the required specifications for 7 nm BEOL. The resulting Cu EM lifetime is 2000X longer than Cu interconnects with a standard scaled barrier/wetting layer. This tCoSFB Cu EM and TDDB reliability performance were equivalent to pure Co metal interconnects, but with a 50% lower line resistance even down to 30 nm pitch dimensions. However, the annealing process for PVD-reflow Cu seed that enhances EM reliability caused Cu agglomeration at dual damascene line-end vias, leading to poor via-chain yield. Resolving this geometry-sensitive via-fill problem was identified as key to extending Cu manufacturability to 7 nm and beyond. We propose, and show preliminary data, for Cu/tCoSFB metallization with CVD Co via pre-fill as potential solution.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"51 1","pages":"T18-T19"},"PeriodicalIF":0.0,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73962392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Tsai, S. Ambrogio, C. Mackin, P. Narayanan, R. Shelby, K. Rocki, A. Chen, G. Burr
{"title":"Inference of Long-Short Term Memory networks at software-equivalent accuracy using 2.5M analog Phase Change Memory devices","authors":"H. Tsai, S. Ambrogio, C. Mackin, P. Narayanan, R. Shelby, K. Rocki, A. Chen, G. Burr","doi":"10.23919/VLSIT.2019.8776519","DOIUrl":"https://doi.org/10.23919/VLSIT.2019.8776519","url":null,"abstract":"We report accuracy for forward inference of long-short-term-memory (LSTM) networks using weights programmed into the conductances of $> 2.5text{M}$ phase-change memory (PCM) devices. We demonstrate strategies for software weight-mapping and programming of hardware analog conductances that provide accurate weight programming despite significant device variability. Inference accuracy very close to software-model baselines is achieved on several language modeling tasks.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"47 31 1","pages":"T82-T83"},"PeriodicalIF":0.0,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80623357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Hu, Hung-Han Lin, Z. Zheng, Zih-Tang Lin, Yi-chun Lu, Lun-Yi Ho, Yen-Wei Lee, Cheng-Wei Su, C. Su
{"title":"Split-Gate FeFET (SG-FeFET) with Dynamic Memory Window Modulation for Non-Volatile Memory and Neuromorphic Applications","authors":"V. Hu, Hung-Han Lin, Z. Zheng, Zih-Tang Lin, Yi-chun Lu, Lun-Yi Ho, Yen-Wei Lee, Cheng-Wei Su, C. Su","doi":"10.23919/VLSIT.2019.8776555","DOIUrl":"https://doi.org/10.23919/VLSIT.2019.8776555","url":null,"abstract":"In this work, we propose a novel split-gate FeFET (SG-FeFET) with two separate external gates to dynamically modulate the memory window (MW) for non-volatile memory and neuromorphic applications. During read operation, only one gate is turned on to decrease the area ratio $(text{A}_{text{FE}}/text{A}_{text{IL}})$ of ferroelectric layer to insulator layer, which increases MW and read current ratio $(text{I}_{text{Read}_{-}1}/text{I}_{text{Read}_{-}0})$. During write operation (program/erase), both two gates are turned on to increase $text{A}_{text{FE}}/text{A}_{text{IL}}$, which decreases MW, thereby resulting in lower write voltage $(text{V}_{text{Write}})$. Compared to FeFET, SG-FeFET (1) Demonstrates lower $text{V}_{text{Write}}(=1.85text{V})$ and 59.5% reduction in write energy at fixed $text{I}_{text{Read}_{-}1}/text{I}_{text{Read}_{-}0};(2)$ Exhibits lower read energy (-11.3%) and higher $text{I}_{text{Read}_{-}text{1}/text{I}_{text{Read}_{-}0}}(=8.6text{E}6)$ at fixed $text{V}_{text{Write}};(3)$ Allows random access and eliminates half-select disturb; (4) Preserves higher endurance due to lower $text{V}_{text{Write}}$ and charge trapping. SG-FeFET as synaptic device also exhibits superior symmetry and linearity for potentiation and depression process.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"68 1","pages":"T134-T135"},"PeriodicalIF":0.0,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82492259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Lue, C. Hsieh, T. Hsu, W. C. Chen, C. Chen, C. Chiu, Keh-Chung Wang, Chih-Yuan Lu
{"title":"Advantage of Extremely-thin Body (Tsi~3nm) Device to Boost the Memory Window for 3D NAND Flash","authors":"H. Lue, C. Hsieh, T. Hsu, W. C. Chen, C. Chen, C. Chiu, Keh-Chung Wang, Chih-Yuan Lu","doi":"10.23919/VLSIT.2019.8776483","DOIUrl":"https://doi.org/10.23919/VLSIT.2019.8776483","url":null,"abstract":"The advantage of using extremely-thin body (ETB, Tsi=3nm) device has been demonstrated in a 3D NAND Flash test chip. Net P/E memory window gain of >1.3V is observed for devices using ETB poly-Si. This substantial gain can be explained by the “quantum confinement” that raises effective Si bandgap and in turn reduces the tunneling barrier height. Simulation model has been validated and it shows equivalent barrier height reduction of ~0.16eV and 0.07eV for electron and hole, respectively for Tsi=3nm. Meanwhile, the extremely-thin body poly silicon channel can improve S.S. to nearly 250mV/dec, which is close to bulk 2D Flash devices. However, the Idsat is degraded to only 160nA for Tsi=3nm, which is attributed to the larger effective mass or higher contact resistance. The degraded Idsat can be accommodated by lower Isense<30nA for page buffer circuit tuning. Random telegraph noise (RTN) is significantly reduced by extremely-thin body, and it shows tighter program-verify (PV) distribution in the MLC/TLC operation.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"31 1","pages":"T210-T211"},"PeriodicalIF":0.0,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82525364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Kanhaiya, C. Lau, G. Hills, M. Bishop, M. Shulaker
{"title":"1 Kbit 6T SRAM Arrays in Carbon Nanotube FET CMOS","authors":"P. Kanhaiya, C. Lau, G. Hills, M. Bishop, M. Shulaker","doi":"10.23919/VLSIT.2019.8776563","DOIUrl":"https://doi.org/10.23919/VLSIT.2019.8776563","url":null,"abstract":"We experimentally demonstrate the first static random-access memory (SRAM) arrays based on carbon nanotube (CNT) field-effect transistors (CNFETs). We demonstrate full 1 Kbit 6 transistor (6T) SRAM arrays fabricated with CNFET CMOS (totalling 6,144 p-and n-type CNFETs), with all 1,024 cells functioning correctly without any per-unit customization. We demonstrate robust operation by writing and reading multiple patterns to the Kbit arrays and characterize single-cell SRAM variability (write and read margins) and repeat cycling of cells. Due to low-temperature BEOL-compatible processing, CNFET SRAM enables new opportunities for digital systems, since: (1) CNFET SRAM can be fabricated directly on top of computing logic, and (2) buried power rails (i.e., as in our demonstration where the power rails are fabricated underneath the FET) can potentially enable smaller-area SRAM layouts.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"1 1","pages":"T54-T55"},"PeriodicalIF":0.0,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77582839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ming-Hung Wu, Ming-Chun Hong, Chih-Cheng Chang, P. Sahu, Jeng-Hua Wei, Heng-Yuan Lee, Shyh-Shyuan Shcu, T. Hou
{"title":"Extremely Compact Integrate-and-Fire STT-MRAM Neuron: A Pathway toward All-Spin Artificial Deep Neural Network","authors":"Ming-Hung Wu, Ming-Chun Hong, Chih-Cheng Chang, P. Sahu, Jeng-Hua Wei, Heng-Yuan Lee, Shyh-Shyuan Shcu, T. Hou","doi":"10.23919/VLSIT.2019.8776569","DOIUrl":"https://doi.org/10.23919/VLSIT.2019.8776569","url":null,"abstract":"This work reports the complete framework from device to architecture for deep learning acceleration in an all-spin artificial neural network (ANN) built by highly manufacturable STT-MRAM technology. The most compact analog integrate-and-fire neuron reported to date is developed based on the back-hopping oscillation in magnetic tunnel junctions. This novel device is unique because it performs numerous essential neural functions simultaneously, including current integration, voltage spike generation, state reset, and 4-bit precision. The device itself is also a stochastic binary synapse, and thus eases the implementation of the compact all-spin ANN with high accuracy for online training.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"15 1","pages":"T34-T35"},"PeriodicalIF":0.0,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77614543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Cai, Hyunwoo Park, Jackie Yang, Youseok Suh, Jun Chen, Yandong Gao, Lunwei Chang, John Zhu, S. C. Song, Jihong Choi, Gary Chen, Bo Yu, Xiao-Yong Wang, V. Huang, Gudoor Reddy, Nagaraj Kelageri, D. Kidd, P. Pénzes, W. Chung, S. Yang, S.B. Lee, B. Tien, G. Nallapati, S. Wu, P. Chidambaram
{"title":"7nm Mobile SoC and 5G Platform Technology and Design Co-Development for PPA and Manufacturability","authors":"M. Cai, Hyunwoo Park, Jackie Yang, Youseok Suh, Jun Chen, Yandong Gao, Lunwei Chang, John Zhu, S. C. Song, Jihong Choi, Gary Chen, Bo Yu, Xiao-Yong Wang, V. Huang, Gudoor Reddy, Nagaraj Kelageri, D. Kidd, P. Pénzes, W. Chung, S. Yang, S.B. Lee, B. Tien, G. Nallapati, S. Wu, P. Chidambaram","doi":"10.23919/VLSIT.2019.8776511","DOIUrl":"https://doi.org/10.23919/VLSIT.2019.8776511","url":null,"abstract":"We report on Qualcomm® Snapdragon™ SDM855 mobile SoC and world's first commercial 5G platform using industry-leading 7nm FINFET technologies. SDM855 exhibits $> 30%$ CPU performance gain over the previous generation thanks to a new design architecture enabled by dual poly pitch process integration. Low voltage operation and tight spread in power consumption has been achieved through process and design co-development, delivering a high performance and low power solution for both mobile and AI applications. Extending the 7nm technology with 2nd-year process enhancement demonstrates up to 50mV CPU Vmin reduction without any change to design rules, which paves the road for an integrated 5G mobile platform with $> 10text{Gbps}$ connectivity.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"40 1","pages":"T104-T105"},"PeriodicalIF":0.0,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74797451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Schuddinck, O. Zografos, P. Weckx, P. Matagne, S. Sarkar, Y. Sherazi, R. Baert, D. Jang, D. Yakimets, A. Gupta, B. Parvais, J. Ryckaert, D. Verkest, A. Mocuta
{"title":"Device-, Circuit- & Block-level evaluation of CFET in a 4 track library","authors":"P. Schuddinck, O. Zografos, P. Weckx, P. Matagne, S. Sarkar, Y. Sherazi, R. Baert, D. Jang, D. Yakimets, A. Gupta, B. Parvais, J. Ryckaert, D. Verkest, A. Mocuta","doi":"10.23919/VLSIT.2019.8776513","DOIUrl":"https://doi.org/10.23919/VLSIT.2019.8776513","url":null,"abstract":"The structure of the complementary FET (CFET) with NMOS stacked on top of PMOS, inherently yields standard cells and SRAM cells with 25% smaller layout area, 25% higher pin density and 2x higher routing flexibility than FinFET with same overall active footprint. Moreover, our work, based on advanced modelling, demonstrates that 4 track CFET can match and even outperform 5 track FinFET; without the need to lower S/D contact resistivity down to $5text{e}-10Omega.text{cm}^{2}$ or to elevate the channel stress up to 2GPa. All gains in power-performance-area at circuit-level are maintained at block-level, making 4 track CFET a suitable candidate for N3 & N2 technologies. Keywords: CFET, scaling, S/D engineering, Pi-gate.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"38 1","pages":"T204-T205"},"PeriodicalIF":0.0,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91038183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhihong Liu, Hanlin Xie, K. Lee, C. S. Tan, G. Ng, E. Fitzgerald
{"title":"GaN HEMTs with Breakdown Voltage of 2200 V Realized on a 200 mm GaN-on-Insulator(GNOI)-on-Si Wafer","authors":"Zhihong Liu, Hanlin Xie, K. Lee, C. S. Tan, G. Ng, E. Fitzgerald","doi":"10.23919/VLSIT.2019.8776522","DOIUrl":"https://doi.org/10.23919/VLSIT.2019.8776522","url":null,"abstract":"GaN-on-Si has revealed its great potential for next-generation power electronics applications, however, there remains a challenge in increasing the breakdown voltage $(BV_{text{off}})$ due to the limit of the GaN epilayer thickness on large size wafers. In this work we propose a GaN-on-Insulator (GNOI)-on-Si structure to address this issue. A 200 mm GNOI-on-Si wafer was prepared through removing the original Si substrate of a GaN-on-Si wafer and bonding onto a fresh SiO2/Si substrate. HEMTs were fabricated with measured $BV_{text{off}}$ much larger than those on GaN-on-Si. Record high $BV text{off}$ up to 2200 V and high figure-of-merit (FOM) $BV_{off^2}/R_{text{on, sp}}$ up to 1.87 GW/cm2 have been achieved in the HEMTs on a 200 mm GNOI-on-Si wafer with a thin GaN epilayer of $3.2 {mu m}$.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"47 1","pages":"T242-T243"},"PeriodicalIF":0.0,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88314157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Srinivasa, Yung-Ning Tu, Xin Si, Cheng-Xin Xue, Chun-Ying Lee, F. Hsueh, Chane-Hone Shen, J. Shieh, W. Yeh, A. Ramanathan, M. Ho, J. Sampson, Meng-Fan Chang, V. Narayanan
{"title":"Monolithic 3D+ -IC based Reconfigurable Compute-in-Memory SRAM Macro","authors":"S. Srinivasa, Yung-Ning Tu, Xin Si, Cheng-Xin Xue, Chun-Ying Lee, F. Hsueh, Chane-Hone Shen, J. Shieh, W. Yeh, A. Ramanathan, M. Ho, J. Sampson, Meng-Fan Chang, V. Narayanan","doi":"10.23919/VLSIT.2019.8776506","DOIUrl":"https://doi.org/10.23919/VLSIT.2019.8776506","url":null,"abstract":"This paper presents the first monolithic 3D two-layer reconfigurable SRAM macro capable of executing multiple Compute-in-Memory (CiM) tasks as part of data readout. Fabricated using low cost FinFET based 3D+-IC, the SRAM offers concurrent data read from both layers and write from layer 2 with 0.4V $text{V}_{text{dd}min}$ 12.8x improved computation latency is achieved as compared to near memory computation of successive Boolean operations.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"103 1","pages":"T32-T33"},"PeriodicalIF":0.0,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80297739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}