Monolithic 3D (M3D) integration provides massive vertical integration through the use of nanoscale inter-layer vias (ILVs). However, high integration density and aggressive scaling of the inter-layer dielectric make ILVs especially prone to defects. We present a low-cost built-in self-test (BIST) method that requires only two test patterns to detect opens, stuck-at faults, and bridging faults (shorts) in ILVs. We also propose an extended BIST architecture for fault detection, called Dual-BIST, to guarantee zero ILV fault masking due to single BIST faults and negligible ILV fault masking due to multiple BIST faults. We analyze the impact of coupling between adjacent ILVs arranged in a 1D array in block-level partitioned designs. Based on this analysis, we present a novel test architecture called Shared-BIST with the added functionality of localizing single and multiple faults, including coupling-induced faults. We introduce a systematic clustering-based method for designing and integrating a delay bank with the Shared-BIST architecture for testing small-delay defects in ILVs with minimal yield loss. Simulation results for four two-tier M3D benchmark designs highlight the effectiveness of the proposed BIST framework.

This work addresses how to naturally adopt the l 2 -norm cosine similarity in the neuromemristive system and studies the unsupervised learning performance on handwritten digit image recognition. Proposed architecture is a two-layer fully connected neural network with a hard winner-take-all (WTA) learning module. For input layer, we propose single-spike temporal code that transforms input stimuli into the set of single spikes with different latencies and voltage levels. For a synapse model, we employ a compound memristor where stochastically switching binary-state memristors connected in parallel, which offers a reliable and scalable multi-state solution for synaptic weight storage. Hardware-friendly synaptic adaptation mechanism is proposed to realize spike-timing-dependent plasticity learning. Input spikes are sent out through those memristive synapses to each and every integrate-and-fire neuron in the fully connected output layer, where the hard WTA network motif introduces the competition based on cosine similarity for the given input stimuli. Finally, we present 92.64% accuracy performance on unsupervised digit recognition with only single-epoch MNIST dataset training via high-level simulations, including extensive analysis on the impact of system parameters.

The state-of-the-art Monolithic 3D (M3D) IC design methodologies~\citem3d:Ku-tcad-Compact2D, m3d:Panth-tcad-Shrunk2D use commercial electronic design automation tools built for 2D ICs to implement a pseudo-3D design and split it into two dies that are routed independently to create an M3D design. Therefore, an accurate estimation of 3D wire parasitics at the pseudo-3D stage is important to achieve a well optimized M3D design. In this paper, we present a regression model based on boosted decision tree learning to better predict the 3D wire parasitics (RCs) at the pseudo-3D stage. Our model is trained using individual net features as well as the full-chip design metrics using multiple instantiations of 8 different netlists and is tested on 3 unseen netlists. Compared to the Compact-2D~\citem3d:Ku-tcad-Compact2D flow on its own as the reference pseudo-3D, the addition of our predictive model achieves up to $2.9 \times$ and $1.7 \times$ smaller root mean square error in the resistance and capacitance predictions respectively. On an unseen netlist design, we observe that our model provides 98.6% and 94.6% RC prediction accuracy in 3D and up to $6.4 \times$ smaller total negative slack of the design compared to the result of Compact-2D flow resulting in a more timing-robust M3D IC. This model is not limited to Compact-2D, and can be extended to other pseudo-3D flows.

Studies have shown that monolithic 3D ( M3D ) ICs outperform the existing through-silicon-via ( TSV ) -based 3D ICs in terms of power, performance, and area ( PPA ) metrics, primarily due to the orders of magnitude denser vertical interconnections offered by the nano-scale monolithic inter-tier vias. In order to facilitate faster industry adoption of the M3D technologies, physical design tools and methodologies are essential. Recent academic efforts in developing an EDA algorithm for 3D ICs, mainly targeting placement using TSVs, are inadequate to provide commercial-quality GDS layouts. Lately, pseudo-3D approaches have been devised, which utilize commercial 2D IC EDA engines with tricks that help them operate as an efficient 3D IC CAD tool. In this article, we provide thorough discussions and fair comparisons (both qualitative and quantitative) of the state-of-the-art pseudo-3D design flows, with analysis of limitations in each design flow and solutions to improve their PPA metrics. Moreover, we suggest a hybrid pseudo-3D design flow that achieves both benefits. Our enhancements and the inter-mixed design flow, provide up to an additional 26% wirelength, 10% power consumption, and 23% of power-delay-product improvements.

In a 2D design, the periphery of a block serves as the optimal pin location since blocks are placed aside horizontally in a single placement layer. However, Monolithic 3D (M3D) integration relieves this boundary constraint by allowing vertical block communication between different tiers based on a nm-scale 3D interconnection pitch. In this paper, we present a design methodology named Pin-in-the-Middle that assigns block pins in the middle of a block using commercial 2D P&R tools to enable efficient block implementation and integration for two-tier block-level M3D ICs. Based on a 28nm two-tier M3D hierarchical design result, we show that our solution offers 13.6% and 24.7% energy-delay-product reduction compared to the M3D design with pins assigned at the block boundaries and its 2D counterpart, respectively.

Despite the recent academic efforts to develop Electronic Design Automation (EDA) algorithms for 3D ICs, the current market does not have commercial 3D computer-aided design (CAD) tools. Insteadpseudo-3D alternative design flows have been devised which utilize commercial 2D CAD engines with tricks that help them operate as a fairly-efficient 3D CAD tool. In this paper we provide detailed discussions and fair power-performance-area (PPA) comparisons of state-of-the-art pseudo-3D design flows. We also analyze the limitations of each design flow and provide solutions with better PPA and various design options. Our experiments using commercial PDK, GDS layouts, and sign-off simulations demonstrate that we achieve up to 26% wirelength and 10% power consumption reduction for pseudo-3D design flows. We also provide a partitioning-first scheme to partitioning-last design flow which increases design freedom with tolerable PPA degradation.

The recent advancement of wafer bonding and monolithic integration technology offers fine-grained 3-D interconnections to face-to-face (F2F) and monolithic 3-D (M3D) ICs. In this article, we propose a full-chip RTL-to-GDSII physical design solution to build commercial-quality two-tier gate-level F2F and M3D ICs. The state-of-the-art flow named shrunk2D (S2D) requires shrinking of standard cells and interconnects by a factor of 50% to fit into the target 3-D footprint of a two-tier design. This, unfortunately, necessitates commercial place/route engines that handle one node smaller geometries, which can be challenging and costly. Our flow named compact-2D (C2D) does not require any geometry shrinking. Instead, C2D implements a 2-D IC with scaled interconnect RC parasitics and contracts the layout to the 3-D integrated circuit footprint. In addition, C2D offers post-tier-partitioning optimization (post-TP opt) which is completely missing in S2D. This additional optimization step is shown to be effective in fixing timing violations caused by inter-tier 3-D routing overhead. Lastly, we present a methodology to reuse the routing result of post-TP opt for the final GDSII generation. Our experimental results show that at iso-performance, C2D offers up to 28.0% power reduction and 15.6% silicon area savings over commercial 2-D ICs without any routing resource overhead.

Monolithic 3D IC overcomes the limitation of the existing through-silicon-via (TSV) based 3D IC by providing denser vertical connections with nano-scale inter-layer vias (ILVs). In this paper, we demonstrate a thorough RTL-to-GDS design flow for monolithic 3D IC, which is based on commercial 2D place-and-route (P&R) tools and clever ways to extend them to handle 3D IC designs and simulations. We also provide a low-cost built-in-self-test (BIST) method to detect various faults that can occur on ILVs. Lastly, we present a resistive random access memory (ReRAM) compiler that generates memory modules that are to be integrated in monolithic 3D ICs.

Monolithic 3D integration provides massive vertical integration through the use of nanoscale inter-layer vias (ILVs). However, high integration density and aggressive scaling of the inter-layer dielectric make ILVs especially prone to defects. We present a low-cost built-in self-test (BIST) method to detect opens, stuck-at faults (SAFs), and bridging faults (shorts) in ILVs. Two test patterns-all-1s and all-0s-are applied to the input side of a set of ILVs (e.g., making up a bus between two tiers). On the adjacent tier (the output side of the ILVs), the test responses are compacted to a 2-bit signature through space compaction. We prove that this compaction solution does not introduce any fault aliasing. Simulations results using HSPICE and M3D benchmark designs show that the proposed BIST method requires low area overhead and test time, but provides effective fault localization and the detectability of a wide range of resistive faults.

A liquid state machine (LSM) is a powerful recurrent spiking neural network shown to be effective in various learning tasks including speech recognition. In this work, we investigate design and architectural co-optimization to further improve the area-energy efficiency of LSM-based speech recognition processors with monolithic 3D IC (M3D) technology. We conduct fine-grained tier partitioning, where individual neurons are folded, and explore the impact of shared memory architecture and synaptic model complexity on the power-performance-area-accuracy (PPAA) benefit of M3D LSM-based speech recognition. In training and classification tasks using spoken English letters, we obtain up to 70.0% PPAA savings over 2D ICs.

As small-form-factor and low-power end devices matter in the cloud networking and Internet-of-Things Era, the bio-inspired neuromorphic architectures attract great attention recently in the hope of reaching the energy-efficiency of brain functions. Out of promising solutions, a liquid state machine (LSM), that consists of randomly and recurrently connected reservoir neurons and trainable readout neurons, has shown a great promise in delivering brain-inspired computing power. In this work, we adopt the state-of-the-art face-to-face (F2F)-bonded 3D IC flow named Compact-2D [4] to the LSM processor design, and study the power-area-accuracy benefits of 3D LSM ICs targeting the next generation commercial-grade neuromorphic computing platforms. First, we analyze how the different size and connection density of a reservoir in the LSM architecture affects the learning performance using the real-world speech recognition benchmark. Also, we explore how much the power-area design overhead should be paid off to enable better classification accuracy. Based on the power-area-accuracy trade-off, we implement a F2F-bonded 3D LSM IC using the optimal LSM architecture, and finally justify that 3D integration practically benefits the LSM processor design in huge form factor and power savings while preserving the best learning performance.

The recent advancement of wafer bonding technology offers fine-grained and silicon-space overhead-free 3D interconnections in face-to-face (F2F) bonded 3D ICs. In this paper, we propose a full-chip RTL-to-GDSII physical design solution to build high-density and commercial-quality two-tier F2F-bonded 3D ICs. The state-of-the-art flow named Shrunk-2D (S2D) requires shrinking of standard cells and interconnects by a factor of 50% to fit into the target 3D footprint of a two-tier design. This, unfortunately, necessitates commercial place/route engines that handle one node smaller geometries, which can be challenging and costly. Our flow named Compact-2D (C2D) does not require any geometry shrinking. Instead, C2D implements a 2D IC with scaled interconnect RC parasitics, and contracts the layout to the F2F design footprint. In addition, C2D offers post-tier-partitioning optimization that is shown to be effective in fixing timing violations caused by inter-tier 3D routing, which is completely missing in S2D. Lastly, we present a methodology to recycle the routing result of post-tier-partitioning optimization for final GDSII generation. Our experimental results show that at iso-performance, C2D offers up to 26.8% power reduction and 15.6% silicon area savings over commercial 2D ICs without any routing resource overhead.

In this paper, we present full-chip designs and their power, performance, and area (PPA) metrics using the ASAP7 process design kit (PDK) and library. Reliable cell library is a key element in evaluating new technological options such as monolithic 3D (M3D) ICs. Given an RTL, we conduct synthesis and place/route to obtain commercial-quality 2D and M3D IC designs and compare PPA. The ASAP7 library is highly useful to build high-quality designs that accurately reflect 7nm technology node. In addition, the full front-end and back-end access provided in ASAP7 allows us to see the impact of various device and interconnect parameters at the full-chip level for both 2D and monolithic 3D ICs. This work demonstrates the critical role of an academic PDK and library in enabling high-quality research in disruptive technologies such as M3D integration.

Existing transistor-level monolithic 3D (T-M3D) standard cell layouts are based on the folding scheme, in which the pull-down network is simply folded and placed on top of the pull-up network. In this paper, we propose a new layout method, the stitching scheme, targeted towards improved cell performance and power integrity. We perform extensive analysis on each layout scheme and evaluate the timing/power benefits of the stitching scheme. Since the ground and power rails overlap in the T-M3D layouts with the folding scheme, we also present a design methodology for the power delivery network of folding T-M3D ICs to evaluate the impact of the T-M3D cell layout scheme on static power integrity. Compared to 2D ICs at iso-performance, stitching T-M3D ICs show a maximum of 6% power savings, 44% area savings with only 1% more static IR-drop in the 14nm technology node while folding T-M3D ICs undergo serious degradation in static power integrity, causing a reliability issue.

In this paper we study power, performance, and cost (PPC) tradeoffs for 2-tier, gate-level, full-chip GDS monolithic 3D ICs (M3D) built using a foundry-grade 7nm bulk FinFET technology. We first develop highly-accurate wafer and die cost models for 2D and M3D to study PPC tradeoffs. In our study, both 2D and M3D designs are optimized in terms of the number of BEOL metal layers used for routing to obtain the best possible PPC values. We develop a new CAD methodology for 2-tier gate-level M3D, named Projected 2D Flow, that allows us to accurately compare RC parasitics of equivalent nets in both 2D and M3D designs. Our experiments based on two different circuit types (BEOL-dominant vs. FEOL-dominant) confirm that M3D designs indeed offer a significant footprint saving. However, to our surprise, the PPC quality of M3D turns out to be worse than that of 2D by 34% due to the high wafer cost of M3D. Our study also reveals that M3D wafer yield should be as high as 90% of 2D wafer yield, and the M3D device manufacturing cost should be less than 33% of that of 2D to justify the adoption of M3D technology at the 7nm era. Lastly, and counter-intuitively, our study shows that FEOL-dominant circuit shows more PPC benefits from M3D technology than BEOL-dominant circuit.

In this paper, we develop physical design tools and methodologies to tackle the inter-tier performance variations caused by low temperature manufacturing in 2-tier gate-level monolithic 3D ICs (M3D). First, we model the top tier front-end-of-line (FEOL) device mobility degradation and its impact on cell delay/power values. Next, we quantify the impact of tungsten interconnect and cost-driven metal layer saving in the back-end-of-line (BEOL) of the bottom tier. These device and interconnect degradation models are used in our new full-chip M3D physical design flow named Derated 2D. This flow overcomes the well-known drawback of the state-of-the-art Shrunk 2D that requires shrinking of layout objects and RC parasitics. Also, Derated 2D performs low-temperature process-aware tier partitioning to effectively keep timing-critical components in the bottom tier. Moreover, Derated 2D conducts timing-driven monolithic inter-tier via (MIV) planning to cope with the resistivity increase in tungsten BEOL. Lastly, Derated 2D offers an effective timing closure solution through a post-route optimization. Experiments based on a foundry-grade 7nm FinFET process design kit (PDK) show that Derated 2D achieves up to 36% performance improvement and 10% energy saving compared with Shrunk 2D. Using a post-route optimization, Derated 2D further improves timing under various FEOL/BEOL degradation settings at a minimum energy overhead.

3D DRAM is the next-generation memory system targeting high bandwidth, low power, and small form factor. This paper presents a cross-domain CAD/architectural platform that addresses DC power noise issues in 3D DRAM targeting stacked DDR3, Wide I/O, and hybrid memory cube technologies. Our design and analysis include both individual DRAM dies and a host logic die that communicates with them in the same stack. Moreover, our comprehensive solutions encompass all major factors in design, packaging, and architecture domains, including power delivery network wire sizing, redistribution layer routing, distributed, and dedicated TSV placement, die bonding style, backside wire bonding, and read policy optimization. We conduct regression analysis and optimization to obtain high quality solutions under noise, cost, and performance tradeoff. Compared with industry standard baseline designs and policies, our methods achieve up to 68.2% IR-drop reduction and 30.6% performance enhancement.