Abstract Over the past 70 years, the semiconductor industry has undergone transformative changes, largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional (2D) materials like transition metal dichalcogenides (TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

The group testing problem consists of determining a small set of defective items from a larger set of items based on tests on groups of items, and is relevant in applications such as medical testing, communication protocols, pattern matching, and many more. While rigorous group testing algorithms have long been known with runtime at least linear in the number of items, a recent line of works has sought to reduce the runtime to poly(k log n), where n is the number of items and k is the number of defectives. In this paper, we present such an algorithm for non-adaptive group testing termed bit mixing coding (BMC), which builds on techniques that encode item indices in the test matrix, while incorporating novel ideas based on erasure-correction coding. We show that BMC achieves asymptotically vanishing error probability with O(k log n) tests and O(k2· log k · log n) runtime, in the limit as n → ∞ (with k having an arbitrary dependence on n). This closes an open problem of simultaneously achieving poly(k log n) decoding time using O(k log n) tests without any assumptions on k. In addition, we show that the same scaling laws can be attained in a commonly-considered noisy setting, in which each test outcome is flipped with constant probability.

This paper considers several closely-related problems in synchronous dynamic networks with oblivious adversaries, and proves novel $$\varOmega (d + \text{ poly }(m))$$ lower bounds on their time complexity (in rounds). Here d is the dynamic diameter of the dynamic network and m is the total number of nodes. Before this work, the only known lower bounds on these problems under oblivious adversaries were the trivial $$\varOmega (d)$$ lower bounds. Our novel lower bounds are hence the first non-trivial lower bounds and also the first lower bounds with a $$\text{ poly }(m)$$ term. Our proof relies on a novel reduction from a certain two-party communication complexity problem. Our central proof technique is unique in the sense that we consider that communication complexity problem with a special leaker. The leaker helps Alice and Bob in the two-party problem, by disclosing to Alice and Bob certain “non-critical” information about the problem instance that they are solving.

Scheduling to avoid packet collisions is a long-standing challenge in networking, and has become even trickier in wireless networks with multiple senders and multiple receivers. In fact, researchers have proved that even perfect scheduling can only achieve R = O(1/lnN). Here N is the number of nodes in the network, and R is the medium utilization rate.

For dynamic networks with unknown diameter , we prove novel lower bounds on the time complexity of a range of basic distributed computing problems. Together with trivial upper bounds under dynamic networks with known diameter for these problems, our lower bounds show that the complexities of all these problems are sensitive to whether the diameter is known to the protocol beforehand: Not knowing the diameter increases the time complexities by a large poly( N ) factor as compared to when the diameter is known, resulting in an exponential gap. Our lower bounds are obtained via communication complexity arguments and by reducing from the two-party D isjointness CP problem. We further prove that sometimes this large poly( N ) cost can be completely avoided if the protocol is given a good estimate on N . In other words, having such an estimate makes some problems no longer sensitive to unknown diameter.

This paper considers several closely-related problems in synchronous dynamic networks with oblivious adversaries, and proves novel Omega(d + poly(m)) lower bounds on their time complexity (in rounds). Here d is the dynamic diameter of the dynamic network and m is the total number of nodes. Before this work, the only known lower bounds on these problems under oblivious adversaries were the trivial Omega(d) lower bounds. Our novel lower bounds are hence the first non-trivial lower bounds and also the first lower bounds with a poly(m) term. Our proof relies on a novel reduction from a certain two-party communication complexity problem. Our central proof technique is unique in the sense that we consider the communication complexity with a special leaker. The leaker helps Alice and Bob in the two-party problem, by disclosing to Alice and Bob certain "non-critical" information about the problem instance that they are solving.

For dynamic networks with unknown diameter, we prove novel lower bounds on the time complexity of a range of basic distributed computing problems. Together with trivial upper bounds under dynamic networks with known diameter for these problems, our lower bounds show that the complexities of all these problems are sensitive to whether the diameter is known to the protocol beforehand: Not knowing the diameter increases the time complexities by a large poly(N) factor as compared to when the diameter is known, resulting in an exponential gap. Here N is the number of nodes in the network. Our lower bounds are obtained via communication complexity arguments and by reducing from the two-party DisjointnessCP problem. We further prove that sometimes this large poly(N) cost can be completely avoided if the protocol is given a good estimate of N. In other words, having such an estimate makes some problems no longer sensitive to unknown diameter.

This paper considers the problem of computing general commutative and associative aggregate functions (such as Sum) over distributed inputs held by nodes in a distributed system, while tolerating failures. Specifically, there are $N$ nodes in the system, and the topology among them is modeled as a general undirected graph. Whenever a node sends a message, the message is received by all of its neighbors in the graph. Each node has an input, and the goal is for a special root node (e.g., the base station in wireless sensor networks or the gateway node in wireless ad hoc networks) to learn a certain commutative and associate aggregate of all these inputs. All nodes in the system except the root node may experience crash failures, with the total number of edges incidental to failed nodes being upper bounded by f. The timing model is synchronous where protocols proceed in rounds. Within such a context, we focus on the following question: Under any given constraint on time complexity, what is the lowest communication complexity, in terms of the number of bits sent (i.e., locally broadcast) by each node, needed for computing general commutative and associate aggregate functions?

Multi-party communication complexity involves distributed computation of a function over inputs held by multiple distributed players. A key focus of distributed computing research, since the very beginning, has been to tolerate failures. It is thus natural to ask “If we want to compute a certain function in a fault-tolerant way, what will the communication complexity be?” For this question, this article will focus specifically on (i) tolerating node crash failures, and (ii) computing the function over general topologies (instead of, e.g., just cliques). One way to approach this question is to first develop results in a simpler failure-free setting, and then “amend” the results to take into account failures' impact. Whether this approach is effective largely depends on how big a difference failures can make. This article proves that the impact of failures is significant, at least for the Sum aggregate function in general topologies: As our central contribution, we prove that there exists (at least) an exponential gap between the non-fault-tolerant and fault-tolerant communication complexity of S um . This gap attests that fault-tolerant communication complexity needs to be studied separately from non-fault-tolerant communication complexity, instead of being considered as an “amended” version of the latter. Such exponential gap is not obvious: For some other functions such as the M ax aggregate function, the gap is only logarithmic. Part of our results are obtained via a novel reduction from a new two-party problem U nion S ize CP that we introduce. U nion S ize CP comes with a novel cycle promise , which is the key enabler of our reduction. We further prove that this cycle promise and U nion S ize CP likely play a fundamental role in reasoning about fault-tolerant communication complexity.

Multi-party communication complexity involves distributed computation of a function over inputs held by multiple distributed players. A key focus of distributed computing research, since the very beginning, has been to tolerate crash failures. It is thus natural to ask "If we want to compute a certain function in a fault-tolerant way, what will the communication complexity be?" This natural question, interestingly, has not been formally posed and thoroughly studied prior to this work.

Motivated by recent emerging systems that can leverage partially correct packets in wireless networks, this paper proposes the novel concept of error estimating coding (EEC). Without correcting the errors in the packet, EEC enables the receiver of the packet to estimate the packet's bit error rate, which is perhaps the most important meta-information of a partially correct packet. Our EEC design provides provable estimation quality with rather low redundancy and computational overhead. To demonstrate the utility of EEC, we exploit and implement EEC in two wireless network applications, Wi-Fi rate adaptation and real-time video streaming. Our real-world experiments show that these applications can significantly benefit from EEC.

Motivated by recent emerging systems that can leverage partially correct packets in wireless networks, this paper investigates the novel concept of error estimating codes (EEC). Without correcting the errors in the packet, EEC enables the receiver of the packet to estimate the packet's bit error rate, which is perhaps the most important meta-information of a partially correct packet. Our EEC algorithm provides provable estimation quality, with rather low redundancy and computational overhead. To demonstrate the utility of EEC, we exploit and implement EEC in two wireless network applications, Wi-Fi rate adaptation and real-time video streaming. Our real-world experiments show that these applications can significantly benefit from EEC.