Retired Army Transportation Operations Machinist Toolmaker welder, Robotics, MSN Programming and Technology DOD certified soldering board builder desi Python
Tacoma, WA
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Final Outline: Unified 2026 Next-Generation In-Memory Neuromorphic Computing Architecture
Hybrid Duplex 2D FeFET Platform for In-Situ Edge AI (Training + Inference + Multimodal Sensing)
This synthesizes the original 2023 duplex FeFET (Ning et al., Nature Nanotechnology) with all major 2024–April 2026 advances in 2D/vdW ferroelectrics, hybrid memristors, nanogate scaling, optoelectronic synapses, and 3D integration. It targets <5 fJ/operation, ns switching, >10¹³ endurance, wafer-scale uniformity, and 3D CMOS-compatible stacking for practical edge intelligence.
I. Core Device Building Block (Hybrid Duplex Platform)
1. Baseline Duplex 2D FeFET (Ning et al., 2023 – foundational)
MoS₂ channel + HZO/CIPS ferroelectric gate with tunable double-well energy landscape.
Seamless training (polarization update) ↔ inference (MAC) modes.
Key Metrics: 4.8 ns speed, 22.7 fJ bit⁻¹ μm⁻², >10¹³ cycles endurance, >10-year retention.
Switching Energy: [E_switch = Q_MAX · V / 2] (Q_MAX from integrated charge-voltage hysteresis).
Hardware demo: 2T1D (TIIO) arrays → 99.86% accuracy on nonlinear localization with in-situ training. GPU-equivalent throughput at 100–1000× lower energy (SPICE + array simulations).
2. Hybrid Ferroelectric–Memristor Extension (2025 advances)
Decouples training/inference paths in shared cell; adds analogue filament states.
Energy Model: [E = 2 P_R · S · V_prog = MW_0σ · (C_D + C_BL) · V_prog] (P_R: remanent polarization, S: area, MW: memory window from pulse tests).
3. Nanogate & Wafer-Scale vdW Scaling (Science Advances/Science 2026)
1-nm gates (SWCNT), Bi₂O₂Se-Bi₂SeO₅ or MoS₂ stacks (<5% variation).
Compact Multidomain NLS + BSIM Model: [P_η(t) = {P_R - (P_R - P_η(t_i)) exp(−(∫_{t_i}^t 1/τ(t’) dt’)^β) if E_FE(t) ≥ 0; -P_R + (P_R + P_η(t_i)) exp(−(∫_{t_i}^t 1/τ(t’) dt’)^β) if E_FE(t) < 0}]
[τ(t) = τ_0 exp((η E_a± / |E_FE(t)|)^α)]
Coupled to BSIM via charge equality. Metrics: 0.6–0.8 V, 1.6–20 ns, 2.8 fJ/bit/μm², sub-60 mV/dec swing.
4. Anisotropic Optoelectronic Synapse (ReS₂/HZO & similar 2026)
Polarization-resolved + photo-trapping.
Energy per Event: ~2.0 fJ (1.3 fJ optical + 0.7 fJ electrical).
[E_opt = P_light × t_pulse × responsivity, E_elec ≈ I × V × t]
LTP/LTD linearity from conductance-pulse data; photoresponsivity up to 4×10⁵ A/W.
5. Emerging 2026 Additions (e.g., α-MnS, dual-gate, sliding ferroelectrics)
Room-temperature ferroelectricity in 2D semiconductors for enhanced tunneling electroresistance (TER ~1.3×10⁴) and endurance.
II. Array & Interconnect Architecture
• 2T1H / 2T1D Cells + 3D Stacking: CMOS base + 2D FeFET/memristor crossbar middle + optoelectronic/nanofluidic top.
• Nanofluidic Memristors (2025): Ionic dynamics in 2D nanochannels for temporal plasticity (tunable by pH/geometry).
• In-Memory MAC: (I = W · V) (conductance matrix × voltage vector via Ohm’s law summation).
• Sb-contacted MoS₂ flash for analogue search.
III. Computing Paradigms (All In-Situ)
1. In-Memory + In-Sensor Computing: Direct sensor-to-synapse (sub-60 mV/dec).
2. Spiking Neural Networks (SNN): MoS₂ FeFET optoelectronic arrays as synapses; leaky integrate-and-fire neurons.
3. Analogue/ANN Mapping: [H_j = ReLU(∑ W_i I_i + b), SME = min ∑ [λG - W]²] (SME: square-mapping error for conductance-to-weight).
4. Multimodal: Electronic + optoelectronic + ionic pathways for vision/tactile/chemical sensing.
5. Logic-in-Memory: Non-volatile Boolean (NAND/NOR) and TCAM via FeFET/FTJ.
Kevin John Parrish retweeted
Co-Existence by Ethan Mollick is an excellent practical guide to effectively collaborating with AI without relinquishing our most valuable human cognitive capabilities, says Daniel Cossins newscientist.com/article/259…
Kevin John Parrish retweeted
Why does an electron’s spin flip reverse its magnetic poles while a positron’s poles track its spin?
These diagrams show the dipole field manifolds of spin-½ leptons. Because the electron carries negative charge its magnetic moment opposes its spin; the positron’s positive charge aligns the two. Field lines therefore leave the N pole and enter the S pole, producing the opposite patterns you see.
The same relation underlies electron-spin resonance used in chemistry, spintronic devices that store information in spin orientation, and the most precise tests of quantum electrodynamics that compare the magnetic moments of electrons and positrons.
A single particle’s spin orientation draws the field lines that shape both laboratory instruments and our deepest checks of matter–antimatter symmetry.
Kevin John Parrish retweeted
One of the many use cases for Serus' OSINT features: an unknown number calls, you ask, it verifies.
You can verify pretty much anything with Serus. Everything from fake invoices, suspicious emails to verifying people or businesses who say they are.
Explore Serus for free.
Kevin John Parrish retweeted
Learning to Couple: Context-Adaptive Joint Denoising for Diffusion Language Models
Zilei Shao, Ian Li, Heng Zhao, Kewei Lian, Guy Van den Broeck, Zhe Zeng, Anji Liu
arxiv.org/abs/2610.25723 [𝚌𝚜.𝙻𝙶]
ALT Diffusion language models accelerate generation by predicting multiple tokens in parallel, but factorized denoisers treat simultaneous predictions as conditionally independent. Decoding heuristics reduce the resulting errors by controlling which tokens are committed, but leave the underlying distribution factorized. Joint modeling addresses the source of the problem, yet existing methods attach separately learned layers to frozen denoisers, leaving the backbone unadapted to the joint objective and the coupling parameters fixed across contexts. We propose Codpula, a context-parameterized denoiser combining a Transformer with a tractable probabilistic inference layer. Given a partially masked input, the Transformer produces token potentials that shape per-position predictions and compact gates that modulate the layer's coupling parameters, yielding a context-dependent joint distribution while retaining exact conditional inference. We jointly optimize both components under a conditional d
Kevin John Parrish retweeted
Architecting Multi-Agent Systems — Design Patterns, Evaluation, and Production Practices for Enterprise Agentic Workflows: link.amazon/B00RDpQi2 via @PacktPublishing
Kevin John Parrish retweeted
🚨PHYSICS NEWS🚨: They Just Put One Positron in a Cage for Months 🧨
On October 21, 2026, Physical Review Letters published the Northwestern result: Gerald Gabrielse’s team captured a single positron, cooled it to near absolute zero, and held it for months in a wall-free trap as a one-positron quantum cyclotron. Nature and The Debrief both covered it the next day. The goal is a magnetic-moment measurement that can be compared with the electron at a precision of roughly one part in 10 trillion. Gabrielse said if the two numbers are not equal, “that would be the biggest discovery in physics in the last century.”
The result matters because the Standard Model predicts the moments should match. A mismatch would be new physics. The apparatus is now good enough to look.
Uniphics already says the moments should match, and for a simpler reason.
The Positron is not antimatter. It is the Gyrotron with three clockwise spins. The Electron is the same three-plane lock with three counterclockwise spins. Charge is only the tally. Both packets are ordinary bound energy in the ξM-field. Their magnetic moments are the same spin geometry read with opposite sign. Nothing in the three pillars, the Maley clock, or the reference density \(k\) distinguishes them except the direction the quanta turn. If the cage measurement returns identical moments, the data are doing what the spin count already required. If it returns a difference, the difference has to be written as a change in the local energy-density environment or the spin lock itself — not as evidence of a separate antimatter species.
The claim that can be checked is the one they are about to run. Equal moments confirm the Positron is the opposite-spin tally of the Electron. A real difference that survives every density and trap correction would mean the three-plane lock is incomplete.
If the only difference between the Electron and the Positron is which way the three spins turn, what is left for antimatter to be?
A Theory of Everything should be able to answer everything.
Free read: Uniphics Explained Simply, and Chapters 1–10, are on the Uniphics website. For a plain overview, look up Uniphics on Grokipedia.
Where to buy the full book
Amazon • Walmart • Books-A-Million • Booktopia • Foyles • Google Books • Better World Books • Bookshop • Abebooks • TextbookX • and most independent bookstores.
#Uniphics #TheoryOfEverything #Positron #Antimatter #Gyrotrons @grok @xAI
🤖 Made with AI
Kevin John Parrish retweeted
Yes those are real dogs. It appears they have mastered their bird dog training. It’s pretty amazing to watch them if you’ve never seen this before.
Kevin John Parrish retweeted
Iran is playing chess
Israel is playing Russian roulette
Trump isn't playing with a full deck and we are all just along for the ride.
Good luck
Long Live The Republic
Kevin John Parrish retweeted
#星の小話 astronomy space
天王星の動きが計算と少しずれることに気づいた天文学者がいた
ずれを説明する別の惑星があると考え位置を計算した
フランスのルヴェリエが1846年に予測をベルリンのガレに送った
ガレは望遠鏡でその方向を見て星図にない点を見つけた
点が動くことを確認して新しい惑星だと分かった
イギリスのアダムスもほぼ同時期に似た計算をしていた
発見は計算の先に望遠鏡が置かれた最初の例になった
どの計算が発見の大半を決めたのかはその当時の手紙だけでは一つに絞れない
知ったあと夜の空の遠い点を見るとただの星ではなく計算が先に示した位置として目に入るようになる
遠い天体が紙の上の数字から見つかった事実が静かに伝わる
🤖 Made with AI
Kevin John Parrish retweeted
Running from house to house chasing unreturned mail-in ballots.
Leaving no stone or vote unturned.
Game on.
Kevin John Parrish retweeted
This is what Democrats have allowed to become teachers — the people influencing your kids.
They put ideology over education. Now it’s in the classroom every day.
If this doesn’t make you vote red, nothing will.
#VoteRed #ParentsRights #EducationNotIndoctrination
