D. Bosch, J. Colinge, G. Ghibaudo, X. Garros, S. Barraud, J. Lacord, B. Sklénard, L. Brunet, P. Batude, C. Fenouillet-Béranger, J. Cluzel, R. Kies, J. Hartmann, C. Vizioz, G. Audoit, F. Balestra, F. Andrieu
{"title":"All-operation-regime characterization and modeling of drain current variability in junctionless and inversion-mode FDSOI transistors","authors":"D. Bosch, J. Colinge, G. Ghibaudo, X. Garros, S. Barraud, J. Lacord, B. Sklénard, L. Brunet, P. Batude, C. Fenouillet-Béranger, J. Cluzel, R. Kies, J. Hartmann, C. Vizioz, G. Audoit, F. Balestra, F. Andrieu","doi":"10.1109/VLSITechnology18217.2020.9265036","DOIUrl":"https://doi.org/10.1109/VLSITechnology18217.2020.9265036","url":null,"abstract":"We evidence a unique feature of junctionless Fully-Depleted Silicon-On-Insulator (JL FDSOI) transistors: the presence of both bulk and accumulation conduction renders standard VT-variability studies incomplete. For JL transistors, we rather propose an original analysis of the (local and global) variability in all-operation-regimes, from subthreshold to accumulation. We evidence that the current variability around VT is highly sensitive to back-bias VB and film thickness $(mathrm{t}_{mathrm{si}})$ uniformity. We demonstrate experimentally for the first time up to 70% lower drain current (ID) local variability for Junctionless Accumulation Mode (JAM) vs. inversion mode (IM) at 1V gate voltage (V G), $mathrm{L}=18$ nm gate length and $mathrm{W}=20mathrm{nm}$ width. This is attributed to the impurity screening, lowering the impact of Random Dopant Fluctuations variability.","PeriodicalId":6850,"journal":{"name":"2020 IEEE Symposium on VLSI Technology","volume":"25 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82845005","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}
A. Tan, M. Pešić, L. Larcher, Y. Liao, Li-Chen Wang, J. Bae, C. Hu, S. Salahuddin
{"title":"Hot Electrons as the Dominant Source of Degradation for Sub-5nm HZO FeFETs","authors":"A. Tan, M. Pešić, L. Larcher, Y. Liao, Li-Chen Wang, J. Bae, C. Hu, S. Salahuddin","doi":"10.1109/VLSITechnology18217.2020.9265067","DOIUrl":"https://doi.org/10.1109/VLSITechnology18217.2020.9265067","url":null,"abstract":"In this work, we demonstrate FDSOI ferroelectric FETs (FeFETs) incorporating 4.5 nm hafnium zirconium oxide, which show a ~0.5V memory window at +/-3.3V and a program/erase speed of 1 $mu mathrm{s}$. In typical FeFETs where $geq 9$ nm thick ferroelectric (FE) gate oxides have been used, bulk charge trapping has been identified as the main mechanism for endurance degradation and shrinkage of the memory window (MW). By contrast, we find that the role of bulk trapping in our devices with a much thinner FE layer is minimal. Through a combination of cryogenic temperature-dependent electrical measurements and simulations using the Ginestra ™ modeling platform, we identify and prove that hot electron-induced hole damage during the application of negative gate biases is the primary source of endurance degradation and MW closure in FeFETs with scaled oxide layers.","PeriodicalId":6850,"journal":{"name":"2020 IEEE Symposium on VLSI Technology","volume":"47 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91350975","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}
C. T. Wang, W. L. Chang, C. Y. Chen, Douglas C. H. Yu
{"title":"Immersion in Memory Compute (ImMC) Technology","authors":"C. T. Wang, W. L. Chang, C. Y. Chen, Douglas C. H. Yu","doi":"10.1109/VLSITechnology18217.2020.9265019","DOIUrl":"https://doi.org/10.1109/VLSITechnology18217.2020.9265019","url":null,"abstract":"Immersion in Memory Compute (ImMC) technology with multiple chips and functions in multi-layer stacking integrated using System on Integrated Chips (SoIC™) technology is presented. The technology provides multiple compute and memory chips to interconnect each other to gain computing power and memory bandwidth. The interconnect parasitics, bandwidth density and power efficiency are analyzed using N7 light IO transceiver. The ImMC compared with 3DIC with bridge and with shared die, respectively, using $mu mathrm{bump}$ and TSV, is studied. The ImMC is 16x, 14x, and 224x better than the 3DIC with bridge in bump density, data rate, and bandwidth density. The transceiver power and size for the ImMC is only 1 % of those for the 3DIC.","PeriodicalId":6850,"journal":{"name":"2020 IEEE Symposium on VLSI Technology","volume":"13 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86465040","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}
U. Celano, Y.H. Chen, A. Minj, K. Banerjee, N. Ronchi, S. Mcmitchell, P. Van Marcke, P. Favia, T.-L. Wu, B. Kaczer, G. Van den bosch, J. van Houdt, P. van der Heide
{"title":"Probing the evolution of electrically active defects in doped ferroelectric HfO2 during wake-up and fatigue","authors":"U. Celano, Y.H. Chen, A. Minj, K. Banerjee, N. Ronchi, S. Mcmitchell, P. Van Marcke, P. Favia, T.-L. Wu, B. Kaczer, G. Van den bosch, J. van Houdt, P. van der Heide","doi":"10.1109/VLSITechnology18217.2020.9265098","DOIUrl":"https://doi.org/10.1109/VLSITechnology18217.2020.9265098","url":null,"abstract":"We correlate the concentration and configuration of electrical defects in ferroelectric Si -doped HfO2 (FE- HfO2) with the electrical device performance during wake-up and fatigue regimes. To this end, we combine time-to-breakdown (TDDB), Kelvin probe force microscopy (KPFM), conductive atomic force microcopy (C-AFM) and Scalpel SPM, probing for the first time, the nanoscopic material variations as a function of device's field cycling behavior.","PeriodicalId":6850,"journal":{"name":"2020 IEEE Symposium on VLSI Technology","volume":"65 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83955533","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}
Chia-Che Chung, Hsin-Cheng Lin, H. H. Lin, W. K. Wan, M. Yang, C. Liu
{"title":"Interpretable Neural Network to Model and to Reduce Self-Heating of FinFET Circuitry","authors":"Chia-Che Chung, Hsin-Cheng Lin, H. H. Lin, W. K. Wan, M. Yang, C. Liu","doi":"10.1109/VLSITechnology18217.2020.9265107","DOIUrl":"https://doi.org/10.1109/VLSITechnology18217.2020.9265107","url":null,"abstract":"An interpretable neural network (NN) is used to model the self-heating (SH) in complex FinFET circuits. The NN training/testing datasets from 3 -stage to 37 -stage chain circuits in folded layout composed of inverter (INV)/NAND/NOR are simulated by our distributed $mathrm{R}_{mathrm{th}}-mathrm{C}_{mathrm{th}}$ SPICE model [1], [2]. The interfacial thermal resistance [3], boundary scattering [4], alloy scattering [5], and layout dependence are considered. The NN interpretation by feature importance analysis is consistent with the thermal physics. Stage# is the most important feature of the NN prediction. Both via2 bundle positions and via2 numbers (via2#) are effective to reduce SH. As compared to SPICE, NN prediction in 37 -stage INV chain computes $3mathrm{x}10^{6}mathrm{X}$ faster with accuracy loss $< 1^{circ}mathrm{C}$. The high computation efficiency and high precision make NN feasible to predict chain circuits up to 40 stages, which cannot be simulated by SPICE due to long computation time.","PeriodicalId":6850,"journal":{"name":"2020 IEEE Symposium on VLSI Technology","volume":"35 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87233371","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. Miura, K. Nishioka, H. Naganuma, T. V. A. Nguyen, H. Honjo, S. Ikeda, T. Watanabe, H. Inoue, M. Niwa, T. Tanigawa, Y. Noguchi, T. Yoshiduka, M. Yasuhira, T. Endoh
{"title":"Scalability of Quad Interface p-MTJ for 1X nm STT-MRAM with 10 ns Low Power Write Operation, 10 years Retention and Endurance $> 10^{11}$","authors":"S. Miura, K. Nishioka, H. Naganuma, T. V. A. Nguyen, H. Honjo, S. Ikeda, T. Watanabe, H. Inoue, M. Niwa, T. Tanigawa, Y. Noguchi, T. Yoshiduka, M. Yasuhira, T. Endoh","doi":"10.1109/VLSITechnology18217.2020.9265104","DOIUrl":"https://doi.org/10.1109/VLSITechnology18217.2020.9265104","url":null,"abstract":"We have firstly fabricated quad-interface perpendicular MTJ (Quad-MTJ) down to 33 nm with our developed PVD, RIE and damage control integration process technologies under 300 mm process. Secondly, we demonstrated scalability merit as well as high speed writing of Quad-MTJ compared with double-interface p-MTJ (Double-MTJ) as follows; (a) two times larger thermal stability factor $Delta(1mathrm{X}$ nm Quad- MTJ is extrapolated to achieve 10 years retention.), (b) lower write voltage at short write pulse regions at less than 30 ns, (c) in scaled MTJ, effective suppression of write current increase for higher write speed, (d) more than 2 times higher write efficiency at 10ns write operation down to 33 nm MTJ. Finally, we revealed that our developed 33 nm Quad-MTJ achieve excellent endurance of more 1011 thanks to higher write efficiency and low damage integration process technology. These results show that the Quad-MTJ technology is one of promising way for low power, high speed and enough reliable STT -MRAM with excellent scalability down to 1X nm node.","PeriodicalId":6850,"journal":{"name":"2020 IEEE Symposium on VLSI Technology","volume":"57 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84341340","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}
Jixuan Wu, Fei Mo, T. Saraya, T. Hiramoto, M. Kobayashi
{"title":"A Monolithic 3D Integration of RRAM Array with Oxide Semiconductor FET for In-memory Computing in Quantized Neural Network AI Applications","authors":"Jixuan Wu, Fei Mo, T. Saraya, T. Hiramoto, M. Kobayashi","doi":"10.1109/VLSITechnology18217.2020.9265062","DOIUrl":"https://doi.org/10.1109/VLSITechnology18217.2020.9265062","url":null,"abstract":"We have monolithically integrated RRAM array with oxide semiconductor channel access transistor in 3D stack, achieved uniform memory characteristics of 1 T1R cells at each layer, and demonstrated basic functionality of XNOR operation as in-memory computing for binary neural network AI applications, for the first time. The impact of RRAM bit error rate on neural network is also investigated. 3D neural network built by this architecture has high potential to enable area-efficient, low-power and low-latency computing.","PeriodicalId":6850,"journal":{"name":"2020 IEEE Symposium on VLSI Technology","volume":"163 7 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86675455","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}
B. C. Paz, L. L. Guevel, M. Cassé, G. Billiot, G. Pillonnet, A. Jansen, R. Maurand, S. Haendler, A. Juge, E. Vincent, P. Galy, G. Ghibaudo, M. Vinet, S. de Franceschi, T. Meunier, F. Gaillard
{"title":"Variability Evaluation of 28nm FD-SOI Technology at Cryogenic Temperatures down to 100mK for Quantum Computing","authors":"B. C. Paz, L. L. Guevel, M. Cassé, G. Billiot, G. Pillonnet, A. Jansen, R. Maurand, S. Haendler, A. Juge, E. Vincent, P. Galy, G. Ghibaudo, M. Vinet, S. de Franceschi, T. Meunier, F. Gaillard","doi":"10.1109/VLSITechnology18217.2020.9265034","DOIUrl":"https://doi.org/10.1109/VLSITechnology18217.2020.9265034","url":null,"abstract":"Variability of28nm FD-SOI transistors is evaluated for the first time down to ultra low temperatures (UL T), at T= 1 00mK. High performance is achieved at UL T for short channel transistors, with $mathrm{I}_{mathrm{ON}} > 1mathrm{mA}mu mathrm{m}$ and $mathrm{I}_{mathrm{OFF}}$ below the equipment accuracy <1 fA, in particular by keeping advantage of forward back biasing (FBB), with the same efficiency from room temperature (R T) down to 100mK. The physical origins of MOSFET mismatch at ULT are studied, highlighting the impact of the charge fluctuations increase on both threshold voltage $(mathrm{V}_{mathrm{TH}})$ and current gain factor $(beta)$ variabilities. Besides that, we demonstrated that the increase of $mathrm{V}_{mathrm{TH}}$ and $beta$ variabilities at low temperature remains reasonably low in comparison to RT values and other CMOS technologies, so that it should not be detrimental to circuit operation in this range of temperatures.","PeriodicalId":6850,"journal":{"name":"2020 IEEE Symposium on VLSI Technology","volume":"40 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85397265","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}
E. Nako, K. Toprasertpong, R. Nakane, Z. Wang, Y. Miyatake, M. Takenaka, S. Takagi
{"title":"Proposal and Experimental Demonstration of Reservoir Computing using Hf0.5Zr0.5O2/Si FeFETs for Neuromorphic Applications","authors":"E. Nako, K. Toprasertpong, R. Nakane, Z. Wang, Y. Miyatake, M. Takenaka, S. Takagi","doi":"10.1109/VLSITechnology18217.2020.9265110","DOIUrl":"https://doi.org/10.1109/VLSITechnology18217.2020.9265110","url":null,"abstract":"We propose a new AI calculation scheme by reservoir computing utilizing the memory effect and nonlinearity of ferroelectric gate MOSFETs (FeFETs) for neuromorphic applications. The task operations of time-series data are experimentally demonstrated by taking time responses of the drain current for gate voltage input as the virtual nodes. A high ability to classify input data is experimentally verified.","PeriodicalId":6850,"journal":{"name":"2020 IEEE Symposium on VLSI Technology","volume":"397 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76609744","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}
D. Okamoto, Y. Suzuki, J. Fujikata, M. Tokushima, J. Ushida, T. Horikawa, K. Takemura, T. Nakamura, Y. Hagihara, K. Yashiki, M. Kurihara, K. Kinoshita, K. Kurata
{"title":"High-Temperature Operation of Chip-Scale Silicon-Photonic Transceiver","authors":"D. Okamoto, Y. Suzuki, J. Fujikata, M. Tokushima, J. Ushida, T. Horikawa, K. Takemura, T. Nakamura, Y. Hagihara, K. Yashiki, M. Kurihara, K. Kinoshita, K. Kurata","doi":"10.1109/VLSITechnology18217.2020.9265051","DOIUrl":"https://doi.org/10.1109/VLSITechnology18217.2020.9265051","url":null,"abstract":"A chip-scale optical transceiver was developed based on silicon photonics technology and optical/electrical assembly for 25-Gbs× four -channel applications. Optical transmitters and receivers were integrated on a single silicon platform enabled by the hybrid integration of a quantum-dot laser diode, optical pins, and a 28-nm CMOS based electrical IC. Temperature compensation functions were implemented in a modulator driver and a transimpedance amplifier for high-temperature operations. The functions allowed us to successfully demonstrate error-free 25 Gb $/mathrm{s}times mathrm{four}$ -channel data transmission at 85°C.","PeriodicalId":6850,"journal":{"name":"2020 IEEE Symposium on VLSI Technology","volume":"1 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89702924","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}