Scalable 2T2R Logic Computation Structure: Design From Digital Logic Circuits to 3-D Stacked Memory Arrays
Bibliographic record
Abstract
In the post Moore era, post-complementary metal–oxide–semiconductor (CMOS) technologies have received intense interests for possible future digital logic applications beyond the CMOS scaling limits. In the meantime, from the system perspective, non-von Neumann architectures, such as processing-in-memory (PIM), are extensively explored to overcome the bottleneck of modern computers, known as the memory wall, for high-performance energy-efficient integrated circuits. In this article, we propose functionally complete nonvolatile logic gates based on a two-transistor-two-resistive random access memory (RRAM) (2T2R) unit structure, which is then used to form a reconfigurable three-transistor-two-RRAM (3T2R) chain with programmable interconnects for complex combinational logic circuits, and a dense 3-D stacked memory array architecture. The design has a highly regular and symmetric structure, while operations are flexible yet simple, without the need of complicated peripheral circuitry or a third resistive state. Implementations of XNOR gate and full adder using 3T2R chain without extra routing/control gates or resistors are shown as demonstration examples of arithmetic unit design. The proposed computing scheme is intrinsic, efficient with superior performance in speed and area. Easily integrated as 3-D stacked array, the proposed memory architecture not only serves as regular 3-D memory array but also performs logic computation within the same layer and between the stacked layers. Concurrent computations under multiple computation modes for flexible operations in the memory are presented. Bias schemes for selected/half-selected/unselected cells are also explained and verified.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".